IOSR

IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE)

ISSN : 2278-1676 Volume 1, Issue 1 (May-June 2012), PP 01-19

www.iosrjournals.org

Review of Graph, Medical and Color Image base Segmentation

Techniques

Patel Janakkumar Baldevbhai1, R.S. Anand

2

1Image & Signal Processing Group, Electrical Engineering Department, Research Scholar, EED, Indian

Institute of Technology Roorkee, Uttarakhand, India,Pin:247 667.

2Electrical Engineering Department, Professor, EED, Indian Institute of Technology Roorkee, Uttarakhand,

India

Abstract—This literature review attempts to provide a brief overview of some of the most common

segmentation techniques, and a comparison between them.It discusses Graph based methods, Medical image

segmentation research papers and Color Image based Segmentation Techniques. With the growing research

on image segmentation, it has become important to categorise the research outcomes and provide readers

with an overview of the existing segmentation techniques in each category. In this paper, different image

segmentation techniques starting from graph based approach to color image segmentation and medical image segmentation, which covers the application of both techniques, are reviewed.Information about open

source software packages for image segmentation and standard databases are provided. Finally, summaries

and review of research work for image segmentation techniques along with quantitative comparisons for

assessing the segmentation results with different parameters are represented in tabular format, which are the

extracts of many research papers.

Index Terms—Graph based segmentation technique, medical image segmentation, color image

segmentation, watershed (WS) method, F-measure, computerized tomography (CT) images

I. Introduction Image segmentation is the process of separating or grouping an image into different parts. There are

currently many different ways of performing image segmentation, ranging from the simple thresholding

method to advanced color image segmentation methods. These parts normally correspond to something that

humans can easily separate and view as individual objects. Computers have no means of intelligently

recognizing objects, and so many different methods have been developed in order to segment images. The

segmentation process in based on various features found in the image. This might be color information, boundaries or segment of an image. The aim of image segmentation is the domain-independent partition of

the image into a set of regions, which are visually distinct and uniform with respect to some property, such as

grey level, texture or colour. Segmentation can be considered the first step and key issue in object

recognition, scene understanding and image understanding.

Its application area varies from industrial quality control to medicine, robot navigation, geophysical

exploration, military applications, etc. In all these areas, the quality of the final results depends largely on the

quality of the segmentation. In this review paper we will discuss on graph based segmentation techniques,

color image segmentation techniques and medical image segmentation, which is the real time application and

very important field of research. The mathematical details are avoided for simplicity.

II. SEGMENTATION METHODS

2.1 Graph Based Methods: A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting

several kinds of cluster structure in arbitrary point sets [1][2]. In the year 1971,C. T. Zahn presented a paper,

where brief discussion is made of the application of cluster detection to taxonomy and the selection of good

feature spaces for pattern recognition. Detailed analyses of several planar cluster detection problems are

illustrated by text and figures. The well-known Fisher iris data, in four-dimensional space, have been analysed by these methods.During 1993,N.R. Pal and S.K. Pal [3] presented a review paper on image

segmentation techniques. They mentioned that, selection of an appropriate segmentation technique largely

depends on the type of images and application areas. It has been established by Pal and Pal that the grey level

distributions within the object and the background can be more closely approximated by Poisson

distributions than that by the normal distributions. An interesting area of investigation is to find methods of

Review of Graph, Medical and Color Image base Segmentation Techniques

www.iosrjournals.org 2 | Page

objective evaluation of segmentation results. It is very difficult to find a single quantitative index for this

purpose because such an index should take into account many factors like homogeneity, contrast,

compactness, continuity, psycho-visual perception, etc. Possibly the human being is the best judge for

this.N.R. Pal and D. Bhandari [4] presented a paper in 1993. The segmentation of an image is a partitioning

of the picture into connected subsets, each of which is uniform, but no union of adjacent subsets is uniform.

Kittler and Illingworth suggested an iterative method for minimum error thresholding assuming normal distributions for the gray level variations within the object and background. The computational steps required

implementing the method of Kittler and Illingworth have been derived assuming Poisson distribution for the

gray level variation within the object/background. The method proposed by pal & pal requires less

computation time than the method of Kittler and Illingworth. Attempts have also been made for the objective

evaluation of the segmented images using measures of correlation, divergence, entropy and uniformity.

Objective evaluation of the thresholds has been done by them using divergence, region uniformity,

correlation between original image and the segmented image and second order entropy.In his paper (1996),

Y. J. Zhang[5] investigates how the results of object feature measurement are affected by image

segmentation. Seven features have been examined by Yu Jin Zhang under different image conditions using a

common segmentation procedure. The results indicate that accurate measures of image properties profoundly

depend on the quality of image segmentation. As there is currently no general segmentation theory, the empirical methods are more suitable and useful than the analytical methods for performance evaluation of

segmentation algorithms [6]. A number of controlled tests are carried out to examine the dependence of

feature measurements on the quality of segmented images. In the year 1997, Mark Tabb et al.[7] proposed an

algorithm for image segmentation at multiple scales, concerned with the detection of low level structure in

images. It describes an algorithm for image segmentation at multiple scales. Jianbo Shi and Jitendra Malik

[8] in the year 2000, proposed a novel approach for solving the perceptual grouping problem in vision.

Normalized cut is an unbiased measure of disassociation between subgroups of a graph and it has the nice

property that minimizing normalized cut leads directly to maximizing the normalized association, which is an

unbiased measure for total association within the subgroups. Bir Bhanu and Jing Peng[9]presented a general

approach to image segmentation and object recognition that can adapt the image segmentation algorithm

parameters to the changing environmental conditions. Rather than focusing on local features and their

consistencies in the image data, their approach aims at extracting the global impression of an image. They treat image segmentation as a graph partitioning problem and proposed a novel global criterion, the

normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity

between the different groups as well as the total similarity within the groups.Leo Grady and Eric L. Schwartz

[10] introduced in the year 2006 an alternate idea that finds partitions with a small isoperimetric constant,

requiring solution to a linear system rather than an eigenvector problem. Suggestions for future work are

applications to segmentation in space-variant architectures, supervised or unsupervised learning, three-

dimensional segmentation, and the segmentation/clustering of other areas that can be naturally modelled with

graphs.In Aug. 2010,Lei Zhang et al. [11] first proposed to employ Conditional Random Field (CRF) to

model the spatial relationships among image super pixel regions and their measurements. They then

introduced a multilayer Bayesian Network (BN) to model the causal dependencies that naturally exist among

different image entities, including image regions, edges, and vertices. The CRF model and the BN model are then systematically and seamlessly combined through the theories of Factor Graph to form a unified

probabilistic graphical model that captures the complex relationships among different image entities. Using

the unified graphical model, image segmentation can be performed through a principled probabilistic

inference.

2.2 COLOR IMAGE SEGMENTATION: Healey [12] includes the edge information to guide the colour segmentation process, while in [13]

the authors combine the texture features extracted from each sub-band of the colour space with the colour

features using heuristic merging rules. In [14], the authors discuss a method that employs the colour–texture information for the model-based coding of human images, while Shigenaga [15] adds the spatial frequency

texture features sampled by Gabor filters to complement the CIE Lab (CIE is the acronym for the

Commission International d‘Eclairage) colour image information. In order to capture the colour–texture

content, Rosenfeld et al. [16] calculated the absolute difference distributions of pixels in multi-band images,

while Hild et al. [17] proposed a bottom-up segmentation framework where the colour and texture feature

vectors were separately extracted and then combined for knowledge indexing. In papers [113,115–

122,127,128,132,133] extraction of colour image and texture are derived as a sequence of serial processes.

The study of chromatic content has been conducted by Paschos and Valavanis [18]. Shafarenko et al. [19]

explored segmentation of randomly textured colour images. In this approach the segmentation process is



implemented using a watershed transform that is applied to the image data converted to the CIE Luv colour

representation. The application of the watershed transform results in over-segmentation and to compensate

for this problem the resulting regions are merged according to a colour contrast measure until a termination

criterion is met. Hoang et al. [20] proposed a different approach to include the colour and texture information

in the segmentation process and they applied the resulting algorithm to the segmentation of synthetic and

natural images. Their approach proceeds with the conversion of the RGB image into a Gaussian colour model and this is followed by the extraction of the primary colour–texture features from each colour channel using a

set of Gabor filters. They applied Principal Component Analysis (PCA) to reduce the dimension of the

feature space from sixty to four. The resulting feature vectors are used as inputs for a K-means algorithm that

is employed to provide the initial segmentation that is further refined by a region-merging procedure.

Segmentation results are obtained by the method using a set of images from Berkeley database [21]. Wang et

al. [22] proposed quaternion Gabor filters to sample the colour–texture properties in the image. In their

approach the input image is initially converted to the Intensity Hue Saturation (IHS) colour space and then

transformed into a reduced biquaternion representation. The paper presented by Jain and Healey [23] where

a multi-scale classification scheme in the colour–texture domain has been investigated. The experiments

were conducted using several images from the MIT VisTex database (Vision Texture—Massachusetts

Institute of Technology) [24] and the segmentation results were visually compared against those returned by JSEG (J-image Segmentation) proposed by Deng and Manjunath [25].

2.2.1 VARIOUS APPROACHES FOR COLOUR EXTRACTION

Among various strategies, the evaluation of the colour attributes in succession was one of the most

popular directions of research. A segmentation algorithm has been proposed by Mirmehdi and Petrou [26]. In

this paper the authors introduced a colour–texture segmentation scheme where the feature integration is

approached from a perceptual point of view. Here, convolution matrices are calculated using a weighted sum

of Gaussian kernels and are applied to each colour plane of the opponent colour space (intensity, red–green

and blue–yellow planes, respectively) to obtain the image data that make-up the perceptual tower. A related

segmentation approach was proposed by Huang et al. [27]. In this paper the authors applied Scale Space

Filters (SSF) to partition the image histogram into regions that are bordered by salient peaks and valleys. A

well-known technique of colour–texture feature integration approach was proposed by Deng and Manjunath [25] and this algorithm is widely regarded as a benchmark by the computer vision community. The proposed

method is referred to as JSEG and consists of two computational stages, namely colour quantisation and

spatial segmentation. The segmentation process is defined as a multi-scale region growing strategy that is

applied to the J-images, where the initial seeds required by the region growing procedure correspond to

minima of local J values. This multi-scale region growing process often generates an over-segmented result,

and to address this problem, a post-processing technique is applied to merge the adjacent regions based on

colour similarity and the Euclidian distance in the CIE Luv colour space. In general the overall performance

of the JSEG algorithm is very good. Results depend on the optimal selection of three parameters specified by

the user: the colour quantisation threshold, the number of scales and the merge threshold. They evaluated the

performances of the new JSEG-based algorithm and the original JSEG implementation when applied to 150

images randomly chosen from the Berkeley database [21]. In [28,29] the authors replaced the colour quantisation phase of the JSEG algorithm with an adaptive Mean Shift clustering method, while Zheng et al.

[30] followed the same idea and combined the quantisation phase of the JSEG algorithm with fuzzy

connectedness. Yu et al. [31] attempted to address the over-segmentation problems associated with the

standard JSEG algorithm. The authors validated the proposed algorithm on 200 images from the Berkeley

database [21] and they reported that the Local Consistency Error (LCE). Krinidis and Pitas [32] introduced

an approach called MIS (Modal Image Segmentation). The MIS algorithm was evaluated on all 300 images

contained in the Berkeley database [21] using measures such as the Probabilistic Rand (PR) [33], Boundary

Displacement Error (BDE) [35], Variation of Information (VI) [36] and Global Consistency Error (GCE)

[34]. The MIS colour segmentation technique was compared against four state of the art image segmentation

algorithms namely Mean-Shift [26], Normalised Cuts [37], Nearest Neighbour Graphs [27] and Compression

based Texture Merging [25]. The unsupervised segmentation of textured colour images has been recently

addressed in the paper by Hedjam and Mignotte [38].They proposed a hierarchical graph-based Markovian Clustering (HMC) algorithm. The authors have conducted a quantitative performance evaluation of the

proposed technique applied on the Berkeley Database [21] by calculating performance metrics such as

Probabilistic Rand Index (PR) [33], Variation of Information (VI) [36], Global Consistency Error (GCE) [34]

and Border Displacement Error (BDE) [35]. A split and merge image segmentation technique was

implemented for the retrieval of complex images by Gevers [39]. Ojala and Pietikainen [40,41] proposed a

split and merge segmentation algorithm. The split process is evaluated using a metric called Merging

Importance (MI). If the colour distribution is homogenous, the weights w1 and w2 control the contribution of



the texture and colour during the merge process are adjusted in order to give the colour information more

importance. If the colour distribution is heterogeneous it is assumed that the texture is the dominant feature in

the image and the algorithm allocates more weight to texture in the calculation of the MI values. The merge

process is iteratively applied until the minimum value for MI is higher than a pre-defined threshold. Chen

and Chen [42] also implemented split and merge approach for colour–texture segmentation. In this algorithm

a colour quantisation based on a cellular decomposition strategy was applied in the HSV (Hue Saturation Value) colour space to extract the dominant colours in the image. Next, the extraction of the colour and local

edge pattern histograms are performed. P.Nammalwar et al. [43,44] presents a similar strategy for a split and

merge segmentation scheme. Garcia Ugarriza et al. [45] proposed an automatic Gradient SEGmentation

algorithm (referred to as GSEG) for the segmentation. J. Chen et al. [46] implemented an algorithm for the

segmentation application of natural images to content-based image retrieval (CBIR). In [47]G.Paschos and

Valavanis proposed an approach using a region growing procedure. Fondon et al. [48] performed colour

analysis in the CIE Lab space using multi-step region growing and Grinias et al. [49] that proposed a

segmentation framework using K-Means clustering followed by region growing in the spatial domain.

Freixenet et al. [50] proposed to combine the region and boundary information for colour image

segmentation. Sail-Allili and Ziou [51] proposed an approach where the colour–image information is

sampled by compound Gaussian Mixture Models. Another related technique was developed by Luis-Garcia et al. [52], where the local structure tensors and the image colour components were combined within an

energy minimisation framework to accomplish colour–texture segmentation. A graph-based implementation

has been proposed by Han et al. [53]. The colour and texture features are modelled by Gaussian Mixture

Models (GMMs) and integrated into a framework based on the Grab Cut algorithm. Kim and Hong [54] also

defined the colour image segmentation as the problem of finding the minimum cut in a weighted graph. The

experiments were conducted using images selected from the VisTex [24] and Berkeley databases [21]. Level

sets approaches have also been evaluated in the context of colour image analysis in the review presented by

Crème‘s et al. [55]. Active contours in capturing complex geometries and dealing with difficult initialisations

can be found in [56,57]. Brox et al. [58] propose to combine colour, texture and motion in a level-set image

segmentation framework. Zoller et al. [59] formulated the image segmentation as a data clustering process. A

related image segmentation approach was proposed by Ooi and Lim [60]. The clustering methods [61] have

been widely applied in the development of colour image segmentation algorithms due to their simplicity and low computational cost. Colour–texture segmentation framework (referred to as CTex) [62,63], where colour

and texture are investigated on separate channels. The colour image segmentation involves filtering the input

data using a Gradient-Boosted Forward and Backward (GB-FAB) anisotropic diffusion algorithm [64] that is

applied to eliminate the influence of the image noise and improve the local colour coherence. Campbell and

Thomas [65] proposed an algorithm where the extracted colour and texture features are concatenated into a

feature vector and then clustered using a randomly initialised Self Organising Map (SOM) network. The

proposed segmentation technique was tested on natural images taken from the Bristol Image Database [66]

and the segmentation results were compared against manually annotated ground-truth data. Other related

approaches that integrate colour and texture features using clustering methods can be found in [67–73].

Liapis and Tziritas [74] proposed an algorithm for image retrieval where the colour and texture are

independently extracted. Liapis and Tziritas‘ first tests were conducted on Brodatz images [75], a database composed of 112 grayscale textures. In addition, the authors also obtained results using 55 colour natural

images from VisTex [24] and 210 colour images from Corel Photo Gallery. Martin et al. [76] adopted a

different approach for the boundary identification of objects present in natural images. The authors proposed

a supervised learning procedure to combine colour, texture and brightness features by training a classifier

using the manually generated ground-truth data taken from the Berkeley segmentation dataset [21]. The

Normalised Cuts segmentation algorithm [77] and the numerical evaluation was carried out by computing the

mean GCE [34], precision, recall and F-measure values for all images in the Berkeley database [21]. Carson

et al. [78] proposed a colour–texture segmentation scheme (that is referred to as Blobworld) that was

designed to achieve the partition of the input image in perceptual coherent regions. The colour features are

extracted on an independent channel from the CIE Lab converted image that has been filtered with a

Gaussian operator. For automatic colour–texture image segmentation, the authors proposed to jointly model

the distribution of the colour, texture and position features using Gaussian Mixture Models (GMMs). The main advantage of the Blobworld algorithm consists in its ability to segment the image into compact regions.

In this approach proposed by Manduchi [79], he has extracted the colour and texture features on independent

channels, where the mixture models for each class was estimated using an Expectation-Maximisation (EM)

algorithm. Jolly and Gupta [80] followed an approach where the colour and texture features were extracted

on separate channels. An approach was adopted by Khan et al. [81]. In their implementation, the input image

is converted to the CIE Lab colour space prior to the calculation of the colour and texture features. A related

approach was adopted by Fukuda et al. [82] with the purpose of segmenting an object of interest from the



background information. The RGB colour features are combined into a multi-dimensional feature vector with

the local texture features given by the wavelet coefficients. The colour–texture integration is modelled using

Gaussian Mixture Models (GMMs) and the segmentation process is carried out in a coarse-to-fine fashion

using an iterative process to find a minimum cost in a graph. Approaches based on Markov Random Field

(MRF) models were often employed for texture analysis [83–85]. Other approaches that employed MRF

models for colour–texture segmentation include the works detailed in [86–92]. The Local Consistency Error (LCE) and the Global Consistency Error (GCE) [34] are examples of area-based metrics that sample the level

of similarity between the segmented and ground-truth data by summing the local inconsistencies for each

pixel in the image. The paper [93] describes a multiresolution image segmentation algorithm based on

adaptive gradient thresholding, and progressive region growing. The proposed algorithm was tested on the

Berkeley segmentation database of 300 images.The Multiresolution Adaptive and Progressive Gradient-

based colour image SEGmentation (MAPGSEG) results were qualitatively evaluated utilizing the Berkeley

segmentation database consisting of 300 images (of dimensions 321X481), by computing the Normalized

Probabilistic Rand index (NPR)[93]. Table 1 represents quantitative evaluation of research work for

segmentation techniques.

2.3 Video Segmentation: Luciano et al.[94] performed an experiment on real and synthetic sequences in April 2010. In this

paper they experimented with real and synthetic sequences suggest that our method also could be used in

other image processing and computer vision tasks, besides video coding, such as video information retrieval

and video understanding. Their work described an approach for object-oriented video segmentation based on

motion coherence.

2.4 MEDICAL IMAGE BASED RESEARCH:

In the year 1990, Lawrence M. Lifshitz et al.[95] presented a paper on amultiresolution hierarchical

approach to Image Segmentation Based on Intensity Extrema for abdominal CT images. Lawrence &

Stephen presented that Stack-based image segmentation correctly isolates anatomical structures in abdominal

CT images. The aim of their research has been to create a computer algorithm to segment gray scale images

into regions ofinterest (objects). These regions can provide the basis for scene analysis (including shape

parameter calculation) or surface-based shaded graphics display. The algorithm creates a tree structure for image description by defining a linking relationship between pixels in successively blurred versions of the

initial image. In the year 2000,Dzung, Chenyang & Jerry [93] presented critical appraisal of the current status

of semi-automated and automated methods for the segmentation of anatomical medical images and the

methods in medical image segmentation. Tonsillitis disease is the cause of cardiac valvular disease and

pneumonia. Bacteria causing tonsillitis are responsible for future Mitral valve stenosis and pneumonia. These

sequels can be prevented by early diagnosis of disease and finding out the causative pathogens. To improve

data transfer rates, Pranithan Phensadsaeng et al.[96] proposed VLSI architecture by using color model for

early-state tonsillitis detection. Anusha Achuthan et al.[97]presented a new segmentation method that

integrates a wavelet based feature, which is able to enhance the dissimilarity between regions with low

variations in intensity. This feature is integrated to formulate a new level set based active contour model that

addresses the segmentation of regions with highly similar intensities in medical images, which do not have clear boundaries between them. A major difficulty that is specific to the segmentation of MR images is the

‗intensity inhomogeneity artefact, which causes a shading effect to appear over the image.They divided

segmentation methods into eight categories: (a) thresholding approaches, (b) region growing approaches, (c)

classifiers, (d) clustering approaches, (e) Markov random field (MRF) models, (f) artificial neural networks,

(g) deformable models, and (h) atlas-guided approaches. Soumik Ukil et al. [98] developed in Feb. 2009, an

automatic method for the segmentation and analysis of the fissures, based on the information provided by the

segmentation and analysis of the airway and vascular trees. This information is used to provide a close initial

approximation to the fissures, using a watershed transform on a distance map of the vasculature. In a further

refinement step, this estimate is used to construct a region of interest (ROI) encompassing the fissures. The

human lungs are divided into five distinct anatomical compartments called the lobes, which are separated by

the pulmonary fissures. The accurate identification of the fissures is of increasing importance in the early

detection of pathologies, and in the regional functional analysis of the lungs. Soumik Ukil and Joseph M. Reinhardt developed a method to detect incomplete fissures, using a fast-marching based segmentation of a

projection of the optimal surface. A new interactive image processing method is proposed by Zhanli Hu et al.

[99] Using that method, image smoothing, sharpening, histogram processing, pseudo-colour processing,

segmentation, reading, local amplification and measurement for medical image in DICOM format can be

realized. The ROI is enhanced using a ridgeness measure, which is followed by a 3-D graph search to find

the optimal surface within the ROI. The paper (in the year 2010) presented by Yao-Tien Chen [100] proposes



an alternative criterion derived from the Bayesian risk classification error for image segmentation and also

proposed model on a level set method based on the Bayesian risk for medical image segmentation. Atfirst,

the image segmentation is formulated as a classification of pixels. Then the Bayesian risk is formed by the

losses of pixel classification. The proposed model introduces a region-based force determined through the

difference of the posterior image densities for the different classes, a term based on the prior probability

derived from Kullback–Leibler information number, and is gularity term a dopted to avoid the generation of excessively irregular and small segmented regions.Arnau Oliver et al. [101] presented a paper in 2010. They

presented and reviewed different approaches to the automatic and semi-automatic detection and segmentation

of masses in mammographic images. Specific emphasis has been placed on the different strategies. Region-

based segmentation relies on the principle of homogeneity, which means that there has to be at least one

feature that remains uniform for all pixels within a region. Region-based methods can be split in two basic

strategies: the well-known region growing and split and merge approaches. A classification of both detection

and segmentation techniques has been proposed, describing several algorithms and pointing out their specific

features. Dae Sik Jeong et al.[102] presented a paper in the year 2010. They proposed a new iris

segmentation method for non-ideal iris images. This paper proposes a new iris segmentation method that can

be used to accurately extract iris regions from non-ideal quality iris images. Chuan-Yu Chang et al. [103]

proposed a method in June 2010 that includes image enhancement processing to remove speckle noise, which greatly affects the segmentation results of the thyroid gland region obtained from US images. They proposed

a complete solution to estimate the volume of the thyroid gland directly from US images. The radial basis

function neural network is used to classify blocks of the thyroid gland. The integral region is acquired by

applying a specific-region-growing method to potential points of interest. Mustafa et al. [104] presented a

new active contour-based, statistical method for simultaneous volumetric segmentation of multiple

subcortical structures in the brain. Early papers include the work of Funakubo [105] where a region-based

colour–texture segmentation method was introduced for the purpose of biomedical tissue segmentation and

Harms et al. [106] where the colour information has been employed in the computation of texton values for

blood cell analysis. Celenk and Smith [107] proposed an algorithm that integrates the spatial and spectral

information for the segmentation of colour images detailing natural scenes, while Garbay discussed in [108]

a number of methods that were developed for the segmentation of colour bone marrow cell images. Katz et

al. [109] introduced an algorithm for the automatic retina diagnosis where combinations of features including colour and edge patterns were employed to identify the vascular structures in the input image. Table 2

represents quantitative evaluation for medical image based research work.

III. OPEN SOURCE SOFTWARE PACKAGES FOR IMAGE SEGMENTATION

Several open source software packages are available for performing image segmentation [110]. Open

source software packages for performing image segmentation are listed below:

1. ITK - Insight Segmentation and Registration Toolkit (Open Source).

2. ITK-SNAP is a GUI tool that combines manual and semi-automatic segmentation with level sets.

3. GIMP which includes among other tools SIOX (Simple Interactive Object Extraction).

4. VXL is a computer vision library. 5. ImageMagick segments using the Fuzzy C-Means algorithm.

6. 3DSlicer includes automatic image segmentation.

7. MITK has a program module for manual segmentation of gray-scale images.

8. OpenCV is a computer vision library originally developed by Intel.

9. GRASS GIS has the program module i.smap for image segmentation.

10. Fiji - Fiji is just ImageJ, an image processing package which includes different segmentation plug-

ins.

11. AForge.NET - an open source C# framework.

There is also software packages available free of charge for academic purposes:

1. GemIdent 2. CVIPtools

3. MegaWave

Open source software for medical image analysis[110]:

Several open source software packages are available for performing analysis of medical images. Open source software packages for performing analysis of medical images are listed below:



1. ImageJ

2. 3D Slicer

3. ITK

4. OsiriX

5. GemIdent

6. MicroDicom

7. FreeSurfer

8. ClearCanvas

9. Seg3D

10. NumPy + SciPy + MayaVi/Visvis

11. InVesalius

3.1 DATABASES& THEIR WEBSITES Several databases have been proposed by the computer vision community that consist of a large

variety of colour images that can be employed in the quantification of colour image segmentation

algorithms. Standard database is required for implementation of image segmentation techniques or method

on input images. These databases are also useful for evaluation of methods or evaluation of results. It can also be useful for validating the available results by comparisons with the standard database. There are some

publicly available databases containing images with colour image segmentation characteristics. They are

listed below with their respective websites along with the referred research paper.

Name of Databases Database Web address

Berkeley Segmentation Dataset and Benchmark (2001)

[21] http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/

McGill calibrated colour image database (2004) [111] http://tabby.vision.mcgill.ca

Outex database (2002) [112] http://www.outex.oulu.fi/

VisTex database (1995)

[24] http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

Caltech-256 (2007) [113]http://www.vision.caltech.edu/Image_Datasets/Caltech256/

The Prague Texture Segmentation Data Generator and Benchmark (2008) [114] http://mosaic.utia.cas.cz/

Pascal VOC (updated 2009) [115]http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2009/#devkit

CUReT (1999) [116]http://www.cs.columbia.edu/CAVE/software/curet/

SIMPLIcity (2001) [117]http://wang.ist.psu.edu/docs/related/

Minerva (2001) [118]http://www.paaonline.net/benchmarks/minerva/

The Texture Library Database (updated 2009) http://textures.forrest.cz

BarkTex (1998) [119]ftp://ftphost.uni-koblenz.de/outgoing/vision/Lakmann/BarkTex

Corel Commercially available



TABLE 1: QUANTITATIVE EVALUATION OF RESEARCH WORK FOR SEGMENTATION

TECHNIQUES

SR.

NO.

SEGMENTATION

ALGORITHM/PAPER

TITLE

Techniques/Algorithms

/Methods

Quantitative Results

1. Segmentation of colour

texturesMirhmedi an

d Petrou[26]

Edge flow (% misclassified pixels (avg.

error ))

Error per(%) of incorrectly pixels Test Image MM97 Proposed Method

T1 2.8% 0.002%

T2 2.6% 0.06%

T27 11.4% 1.6%

2. Colour texture

segmentation based

on the modal energy

of deformable

surfacesMIS

(Krinidis and Pitas

[32])

Proposed technique MIS

compared with MS,NC

PR BDE VI GCE

Ground truth 0.8754 4.9940 1.1040 0.0797

MIS ᵧ=0 0.7869 8.0930 2.0117 0.2110

MIS ᵧ=50 0.7981 7.8263 1.9348 0.1942

MIS ᵧ=100 0.7912 7.9213 1.9801 0.2022

MS 0.7550 9.7001 2.4770 0.2598

NC 0.7229 9.6038 2.9329 0.2182

NNG 0.7841 9.9497 2.6647 0.1895

3. Automatic image

segmentation by

dynamic region

growth and

multiresolution

merging(GSEG).[45

]

Proposed technique

GSEG compared with

JSEG,GRF

GRF JSEG GSEG

Avg.Time(sec) 240 16 24

Avg.NPR 0.357 0.439 0.495

Std.Dev.NPR 0.345 0.318 0.306

Environment C C MATLAB

4. Colour–texture

segmentation using

unsupervised graph

cuts.UGC .[54]

Proposed technique

UGC compared with

JSEG (Precision-recal,

F-measure (avg)

Human JSEG UGC

Color Color-texton

Precision 0.77 0.42 0.57 0.64

Recall 0.75 0.46 0.49 0.51

F-measure 0.76 0.44 0.53 0.57

5. CTex—an adaptive unsupervised

segmentation

algorithm based on

colour–texture

coherence.[62]

Proposed technique CTex compared with

JSEG

PR Indexmean PR Indexstandard_deviation

JSEG 0.77 0.12

CTex 0.80 0.10

6. Segmentation of

natural images using

self-organising

feature maps. [65]

Area overlap Features Dimensionality Avg. Seg. Accuracy

Texture Only 16 36.4%

Colour Only 3 55.7%

Colour+Texture 19 62.2%

7. B-JSEG(Wang et

al.[120]

Proposed technique B-

JSEG compared with JSEG

JSEG HSEG B-JSEG

Average error 33.1 29.3 24.1 Percentage (%)

8. Unsupervised segmentation of

natural images via

lossy data

compression.CTM

[123]

Proposed technique CTM compared with

MS,NC,FH

PRI VoI GCE BDE Humans 0.8754 1.1040 0.0797 4.994

CTMᵧ=0.1 0.7561 2.4640 0.1767 9.4211

CTMᵧ=0.15 0.7627 2.2035 0.1846 9.4902

CTMᵧ=0.2 0.7617 2.0236 0.1877 9.8962

MS 0.7550 2.477 0.2598 9.7001

NC 0.7229 2.9329 0.2182 9.6038



FH 0.7841 2.6647 0.1895 9.9497

9. Unsupervised

multiscale colour

image segmentation

based on MDL

principle. [126]

F-measure hs hr

4.0 4.5 5.0 5.5 6.0

3.0 0.6587 0.6644 0.6720 0.6713 0.6755

3.5 0.6585 0.6631 0.6698 0.6753 0.6802

4.0 0.6625 0.6684 0.6700 0.6714 0.6801

4.5 0.6624 0.6684 0.6699 0.6734 0.6756

5.0 0.6587 0.6680 0.6706 0.6696 0.6742

10. Colour Image

Segmentation Using Soft Computing

Techniques[127]

Comparison of HCM,

FCM,PFCM and CNN Techniques

PSNR Compression Execution

Ratio time

HCM 28.24 11.15 253.14

FCM 30.57 16.03 1617.1

PFCM 29.53 12.00 2.281

CNN 39.39 17.95 2.125

11. Morphological

Description of Color

Images for

Content-Based

Image

Retrieval[129]

Comparison of

AC,CSD,CDE,CSG,MH

W,MHL to the standard

color histogram

Histogram AC CSD CDE CSG MHW MHL

1 127 89 9 548 470 443

12. Morphological

segmentation on

learned boundaries.(WF and

WS) [132]

Proposed Techniques

WF and WS compared

with NC

Method GCE Precision Recall F-mea.

WF level 2 0.19 0.64 0.37 0.44

NC (6 reg.) 0.29 0.52 0.38 0.42 WS Vol (18 reg.)0.22 0.54 0.60 0.55

NC (18 reg.) 0.23 0.44 0.58 0.48

13. Textured image

segmentation based

on

modulation

models[137]

Proposed Algorithm

compared with

(DCA)+K means,

DCA+WCE

Algorithms Avg.Values Med.Values

of BCE of BCE

Proposed 0.4876 0.5124

DCA+K-Means 0.5454 0.5478

DCA+WCE 0.5044 0.5143

14. An adaptive and

progressive

approach for

efficient gradient-

based multiresolution

color image

segmentation [141]

Proposed technique

MAPGSEG compared

with GRF,JSEG and EG

GRF JSEG EG GSEG MAPGSEG

Avg.Time(sec) 240 16.2 7.0 24.1 11.1

Avg.NPR 0.357 0.439 0.497 0.495 0.495

NPR>0.7 38 65 68 91 85

(# Images)

Environment C C C MATLAB

15. Image Segmentation

with a Unified

Graphical Model

[143]

BN and CRF

Segmentation model

consistency

Algorithm Overall accuracy

TextonBoost 72.2%

Yang et al. 75.1%

Auto-Context 74.5%

Our approach 75.4%



TABLE 2: QUANTITATIVE EVALUATION FOR MEDICAL IMAGE BASED RESEARCH WORK

SR

.

N

O.

SEGMENTATION

ALGORITHM/PAPER

TITLE

Techniques/Algorithms/M

ethods

Quantitative Results

1. Anatomy-Guided

Lung Lobe

Segmentation

in X-Ray CT

Images[98]

An automatic method for

the segmentation of the

lobar fissures on chest CT

scans. The method uses the

interactive watershed

transform, calculated on a vessel distance map, to

obtain an initial

segmentation

Subject Resolution Graph size Time RMS

error

factor X Y Z (s) (mm)

1 1.0 215 93 406 40.06 1.55

2 1.0 222 94 400 27.08 1.88

3 1.0 228 104 442 34.55 1.37 4 1.0 230 123 391 47.47 1.49

5 1.0 229 98 417 32.51 1.70

2. A new iris

segmentation method

that can be used to

accurately extract iris

regions from non-

ideal quality iris

images[102]

Proposed technique a new

iris segmentation method

E1 error rate E2 error rate FPR FNR

2.8 14.4 1.2 27.6

3. Thyroid

Segmentation and

Volume Estimation

in Ultrasound Images[103]

Proposed method copared

with AWMF+ACM and

AWMF+Watershed model

Segmentation Measurement Indices[%]

Methods Accuracy Sensitivity Specificity PPV

NPV

The proposed 96.54 91.98 97.69 91.17 97.91

Method

AWMF+ACM 94.56 85.66 96.90 88.42

92.27

AWMF+

Watershed 88.27 78.80 90.78 70.19

94.14

4. Coupled Non-

Parametric Shape and

Moment-Based Inter-

Shape Pose Priors for Multiple Basal

Ganglia Structure

Segmentation[104]

Quantitative accuracy

results for the 3D

experiments

TPR FPR 1-

DC

C&V 0.7301 0.0075

0.2680 Coupled Shape 0.7299 0.0050

0.2351

Coupled Shape and 0.7291 0.0049

0.2335

Relative pose

5. Image structure

representation and

processing: a

discussion of some

segmentation

methods in

cytology[108]

Compared segmentation

performances as evaluated

according to

Human observer diagnosis

Observer type of diagnosis Correct seg. Uncorrect

seg.

process

Relaxation 70 30

Region Growing 93 7

Frontier detection 89 11

IV. CONCLUSION Color has been widely used for image and video. Since the introduction of color distributions as

descriptors of image content, various research projects have addressed the problems of color spaces,

illumination invariance, color quantization, and color similarity functions. Many different methods have been

developed to enhance the limited descriptive capacity of color distributions. We have presented here the state

of the art of color-based methods that can be used to segment color images. The most surprising element that

emerges from our study of color indexing and segmentation is that most of the methods analysed do not

explore the problem of how to deal with color in a device independent way. Very seldom are details given, or



references made to image acquisition and management in terms of standard color coordinates, although it is

reasonable to assume that the image database contains images acquired from many sources, and subjected to

a number of processing steps before indexing and display. Quantization and segmentation reduce acquisition

noise, and may to some extent cope with changes in imaging conditions, but a more rigorous approach to

color imaging is surely desirable. Several of the algorithms proposed have been designed to implement

machine color constancy, but their application in real world conditions is still under investigation. The very definition of machine color constancy is still a matter for future research, rather than an effective tool that can

be employed in current content-based image segmentation engines. Most color based algorithms make it

possible to perform searches for the presence of specific colours, but not, with very few exceptions, for their

absence. Consequently, it would be desirable to modify segmentation methods so that users can specify

which colours should be excluded from the image segmentation. The definition of feature similarity also

plays fundamental role image segmentation, as images with "similar" feature distributions are often

considered similar in appearance without requiring any semantic expression of this.

The major objective of this paper was to analyse the main directions of research in the field of

colour image segmentation and to categorise the main approaches with respect to the colour image

segmentation process. After evaluating a large number of papers, we identified three major trends in

the development of colour image segmentation, namely algorithms based on feature integration, approaches that integrate the colour and texture attributes in succession and finally methods that extract the colour

image features on independent channels and combine them using various integration schemes. As we

discussed, the methods that fall in the latter categories proved to be more promising when viewed from

algorithmic and practical perspectives. However, since the level of algorithmic sophistication and the

application domain of the newly proposed algorithms are constantly increasing it is very difficult to predict

which approach will dominate the field of colour image analysis in the medium to long term but we believe

that the next generation of algorithms will attempt to bridge the gaps between approaches based on sequential

feature integration and those that extract the colour image features on independent channels. Currently, the

main research area in the field of colour image segmentation is focused on methods that integrate the features

using statistic/probabilistic schemes and methods based on energy minimisation. However in line with the

development of new algorithms an important emphasis should be placed on methodologies that are applied to

evaluate the performance of the image segmentation algorithms. We feel that this issue had not received the attention that it should deserve and as a result the lack of widely accepted metrics by the computer vision

community made the task of evaluating the appropriateness of the developed algorithms extremely difficult.

Although substantial work needs to be done in the area of performance evaluation, it is useful to mention that

most of the algorithms that have been recently published had been evaluated on standard databases and using

well-established metrics. Also, it is fair to mention that the publicly available datasets are not sufficiently

generic to allow a comprehensive evaluation, but with the emergence of benchmark suites such as Berkeley

database this issue starts to finally find an answer. We believe that this review has thoroughly sampled the

field of colour image segmentation using a systematic evaluation of a large number of representative

approaches with respect to feature integration and has also presented a useful overview about past and

contemporary directions of research. To further broaden the scope of this review, we have also provided a

detailed discussion about the evaluation metrics, we examined the most important data collections that are currently available to test the image segmentation algorithms and we analysed the performance attained by

the state of the art implementations. Finally, we cannot conclude this paper without mentioning the

tremendous development of this field of research during the past decade and due to the vast spectrum of

applications, we predict that colour image segmentation analysis will remain one of the fundamental research

topics in the foreseeable future.

Most of the methods described here have been tested on different databases, of very different sizes,

ranging from two to more than thousand images, for different segmentation tasks. This makes it extremely

difficult, if not impossible, to provide an absolute ranking of the effectiveness of the algorithms. We can

make the general observation that color alone cannot suffice to index large, heterogeneous image databases.

The combination of colour with other visual features is a necessary approach that merits further study. Much

more dubious, instead, are methods that assume that affordable, unsupervised image segmentation is always

possible, and that the evaluation of image similarity can be dealt with as a graph matching problem, with a reasonable computational burden. The design of a segmentation system must address issues of efficiency in

addition of those of effectiveness. A promising direction for future research is, in our opinion, the

exploitation of colour image similarity for image database navigation and visualization is an attempt in this

direction and the segmentation of colour images based on psychological effects. We would like to see new

generation systems that support querying by emotions and open-ended searches in the image database where

similar images are located next to each other. We would also like to see new generation systems by

combination of various segmentation techniques (hybrid techniques) along with solving content based query



on real time colour images as well as solving online applications. There is no universal theory on colour

image segmentation still now. All of the existing colour image segmentation approaches are by nature ad

hoc. They are strongly application dependent, in other words, there are no general algorithms and colour

space that are good for all colour images. An image segmentation problem is basically one of psychophysical

perception, and it is essential to supplement any mathematical solutions by a priori knowledge about the

picture knowledge. Most gray level image segmentation techniques could be extended to color image, such as histogram thresholding, clustering, region growing, edge detection and fuzzy based approaches. They can

be directly applied to each component of a colour space, and then the results can be combined in some way to

obtain the final segmentation result. However, one of the problems is how to employ the colour information

as a whole for each pixel. When colour is projected onto three components the colour information is so

scattered that the colour image becomes simply a multispectral image and the colour information that human

can perceive is lost. Another problem is how to choose the colour representation for segmentation, since each

colour representation has its advantages and disadvantages. In most of the existing colour image

segmentation approaches, the definition of a region is based on similar colour. This assumption often makes

it difficult for many algorithms to separate the objects with highlights, shadows, shading or texture which

cause inhomogeneous colours of the objects surface. Using HSI can solve this problem to some extent except

that hue is unstable at low saturation. Some physics based models have been proposed to find the objects boundaries based on the type of materials, but these models have too many restrictions which limit them to

be extensively employed. The fuzzy set theory has attracted more and more attention in the area of image

processing. Fuzzy set theory provides us with a suitable tool which can represent the uncertainties arising in

image segmentation and can model the cognitive activity of the human beings. Fuzzy operators, properties,

mathematics and inference rules IF_THEN rules, have found more and more applications in image

segmentation. Despite the computational cost, fuzzy approaches perform as well as or better than their crisp

counterparts. The more important advantage of a fuzzy methodology lies in that the fuzzy membership

function provides a natural means to model the uncertainty in an image. Subsequently, fuzzy segmentation

results can be utilized in feature extraction and object recognition phases of image processing and computer

vision. Fuzzy approach provides a promising means for colour image segmentation.

In summary, we covered state of art color image segmentation techniques,comparisons of

segmentation techniques, brief about assessment parameters, review of research work in tabular way for quick reference. We have also mentioned many open source software for implementation work for image

segmentation, as well as also mentioned standard databases for algorithms‘ validation purposes, in keeping

mind for larger interest of research community.

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Janak B. Patel (born in 1971) received B.E. (Electronics & Communication Engg from L.D. College

of Engg. Ahmedabad, affiliated with Gujarat University and M.E. (Electronics Communication & System Engg.) in 2000 from Dharmsinh Desai Institute of Technology, Nadiad, affiliated with Gujarat University. He is Asst. Prof. & H.O.D. at L.D.R.P. Institute of Technology & Research, Gandhinagar, and Gujarat. Currently, he is pursuing his Ph.D. program at Indian Institute of Technology, Roorkee under quality improvement program of AICTE, India. His research interest includes digital signal processing, image processing, bio-medical signal and image processing. He has 7 years of industrial and 12 years of teaching experience at Engineering College. He taught many subjects in EC, CS and IT disciplines. He is a life member of CSI, ISTE & IETE.

R R.S. Anand received B.E., M.E. and Ph.D. in Electrical Engg. from University of Roorkee (Indian

Institute of Technology, Roorkee) in 1985, 1987 and 1992, respectively. He is a professor at Indian Institute of Technology, Roorkee. He has published more than 100 research papers in the area of

image processing and signal processing. He has also supervised 10 PhDs, 60 M.Tech.s and also organized conferences and workshops. His research areas are biomedical signals and image processing and ultrasonic application in non-destructive evaluation and medical diagnosis. He is a life member of Ultrasonic Society of India.


ISSN : 2278-1676 Volume 1, Issue 1 (May-June 2012), PP 20-30 www.iosrjournals.org


Analytical approaches for Optimal Placement and sizing of

Distributed generation in Power System

Mohit Mittal*,Rajat Kamboj**,Shivani Sehgal*** *Student of Electrical & Electronics Engg.,Doon Valley Institute of Engg. & Technology,Karnal,India.

**Assistant Professor,Department of EEE, Doon Valley Institute of Engg. & Technology,Karnal,India.

*** Assistant Professor,Department of EEE, Doon Valley Institute of Engg. & Technology,Karnal,India.

Abstract- This work proposes a new algorithm which investigates the performance of Distribution system with multiple DG sources for the reduction in the line loss, by knowing the total number of DG units that the user is

interested to connect. Strategic placement of multiple DG sources for a distribution system planner is a complex

combinatorial optimization problem.

The new and fast algorithm is developed for solving the power flow for radial distribution feeders

taking into account embedded distribution generation sources. Also, new approximation formulas are proposed

to reduce the number of required solution iterations. Power flow techniques (PF) for calculating Network

performance index (NPI),Genetic algorithm in search of best locations, with considering NPI as fitness function.

I. INTRODUCTION The impact of DG in system operating characteristics , such as electric losses, voltage profile, stability

and reliability needs to be appropriately evaluated. The problem of DG allocation and sizing is of great

importance. The installation of DG units at non-optimal places can result in an increase in system losses,

implying in an increase in costs and thereof having an effect of opposite to the desired. For that reason the use

of an optimization method capable of indicating the best solution for a given distribution network can be very

useful for the system planning engineer when dealing with the increase of DG penetration that is happening

nowadays.

II. MATHEMATICAL FORMULATION System Description Consider a three-phase, balanced radial distribution feeder with n buses, I laterals and sublatera

generations. Also, nDG distributed and nC shunt capacitors as shown in figure 331ls.

Fig. 1 : Redial Distribution feeder model including DG and Capacitor

The three recursive branch power flow equations are:

Pi+1 = Pi - ri+1(Pi2+Qi

2) / Vi2- PLi+1+µpAPi+1 (2.1 a)

Qi+1 = Qi - xi+1(Pi2+Qi

2) / Vi2- QLi+1+µpAQi+1 (2.1 b)

Analytical approaches for Optimal Placement and sizing of Distributed generation in Power System


V2i+1 = Vi2- 2(ri+1Pi+xi+1Qi) + (r2

i+1+x2i+1) x (P2

i+Q2i) / Vi

2 (2.1 c)

The following terminal conditions should be satisfied [6]:

i. At the end of the main feeder, laterals and sublaterals as shown in fig.2.1:

Pn = Qn = 0 (2.2)

Pkm = Qkm =0 (2.3)

ii. The voltage at bus k is the same voltage of its lateral i.e:

Vk = Vk0 (2.4)

The real and reactive power losses of each section connecting two buses are:

Plossi+1 = (Pi2 + Qi

2 / Vi2)ri+1 (2.5)

Qlossi+1 = (Pi2 + Qi2/Vi

2)xi+1 (2.6)

III. DG SIZING ISSUES For single DG case, The DG optimal Size will be done by using Analytical Method based on Exact

Loss Formula.

The real power loss in a system is given by

N N

PL - ΣΣ [xij(PiPj + QiQj) + βij(QiPi – PjQj)] (2.7)

i-1 j-1

Where

Xij = 𝑟𝑔

𝑉𝑖𝑉𝑖cos δi− δj ,𝛽 =

𝑛𝑖𝑗

𝑉𝑖𝑉𝑖 δi− δj

Ri + jxij=zij are the ijth element of [ZBus] matrix.

Analytical method is based on the fact that the power loss against injected power is a parabolic function and at

minimum losses the rate of change of losses with respect to injected power becomes zero.

∂PL = 2 𝑥𝑖𝑗 𝑃𝑗 −𝛽𝑖 Q ∙𝑖) =0 (2.8)

∂Pi

Where Pi is the real power injection at node I, which is the difference between real power generation and the real

power demand at that node.

Pi – (iPDGi PDi) (2.9)

Where Pdgi is the real power injection from DG placed node I, and Pli is Load at node i. By combining equations

results in to

PDgi - PDi+1/αij[βijQi-∑i=1j≠iN](αijPj-βijQi) (2.10)

The above equation gives the optimum size of DG for each bus I, for the loss to be minimum. Any

size of DG other than Pdgi placed at bus I, will lead to higher loss. In calculating the optimum sizes of DG at

various locations, using equation (2.10), it was assumed that the values of variable remain unchanged. This

result in small difference between the optimum sizes obtained by this approach and repeated load flow.



IV. PROPOSED ALGORITHM 4.1 Algorithm for Single DG case

The developed algorithm is explained stepwise as follows:

Step 1: First Read the Distribution Network Data and DG size.

Step 2: Give Network Data to Power flow Algorithm to get base case power flow.

Step 3: Save base case power flow for later use.

Step 4: Calculate the Network performance for Different DG.

Step 5: Insert the DG at which the NPI value closer to unity & optimize the DG size.

Step 6: Evaluate NPI with the help of base case power flow and Power flow with single DG inserted case. Step 7: Print results and stop.

4.2 Algorithm for Multiple DG The following Algorithm is developed with the help of New and Fast Power Flow

Solution Algorithm and Genetic Algorithm and is used to get the appropriate results. The developed algorithm is

explained stepwise as follows:

Step 1: First read the Distribution Network Data.

Step 2: Give Network Data to New and Fast Powerflow Algorithm to get base case powerflow.

Step 3: Save base case powerflow for later use.

Step 4: Read Inserting Distributed Generators capacities. [Market available DG sizes 100KW, 220KW,

300KW, 500KW, 750KW, 1KW, 1.6MW]

Step 5: Read Bus numbers for DG insertions from Genetic Algorithm. If this is first time GA will give Initial

population, otherwise GA will give New Population. Step 6: Again apply powerflow for Distribution system with the inserted DG at the position from GA with the

help of New and Fast powerflow Algorithm.

Step 7: Evaluate Multi Objective Index with the help of Base case powerflow and powerflow with DG

inserted.

Step 8: Repeat step 6&7 for all combination of GA population.

Step 9: Check for number.

Step 10: Give NPI values as fitness values to the GA.

Step 11: With the help of fitness function values GA will do the operations (Selection, Crossover, Mutation, and

Elitism) and generate new population next go to step5.

Step 12: Save the n best results and go to next Step

Step 13: For every best result decrease the capacity of DG with fixed % of their individual capacities and do the same for the maximum number of iterations.

Step 14: Lower limit of DG capacity will be 1% of Total load.

Step 15: Run powerflow with for all combinations of new capacities of DG and calculate NPI.

Step 16: Now compare these results with previously saved results fitness values, and print the results according

to the best fitness values.

Step 17: Stop Corresponding Flowchart is as follows:

V. CASE STUDY AND ANALYSIS

5.1 TEST SYSTEM

The radial system with 33 buses and 32 branches with the total load of 3.715 MW and 2.28 MVAR,

11KV is taken as test system. The single line diagram of 33bus distribution system is shown in Figure 6.1.

System Data is given in appendix-B.

19 20 21 22

26 27 28 29 30 31 32 33

S/S

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18



23 24 25

Fig.2. Single line diagram of 33 bus distribution system

5.2 GENETIC ALGORITHM SET UP

Representation

A Binary genetic algorithm (BGA) is employed to generate the combination of bus numbers.

Initialize Population

An initial population of size 30 is selected.

Selection

Tournament Selection is chosen for testing.

Reproduction

Two point Cross Over [0.8], [60% to 95% range] and Binary Mutation of ratio [0.05], [0.5% to 1% range] is used.

Generation-Elitism

Generation Elitism is taken 5, which copies the best chromosomes into next generation.

Fitness Evaluation

The network performance index for the distribution system with DG sources is aimed to be maximum and is

selected for fitness evaluation.

Termination

The algorithm stops if the number of generations reaches 300, each simulation is a fairly lengthy process, but

given that this process is a strategic one, the durations reasonable.

5.3 RELEVANCE FACTORS OF NPI The NPI will numerically describe the impact of DG, considering a given location and size, on a

distribution network. Close to unity values for the Network Performance Index means higher DG benefits. Table

6.0 shows the value for the relevance factors utilized in here, considering a normal operation stage analysis.

ILp W1 ILQ W2 IVDQ W3 IVR W4 VSI W5

0.33 0.10 0.15 0.10 0.32

Table.1.NPI Relevance factors

5.4. Results and analysis

A series of simulations were run to evaluate the performance of Distribution System With a defined

number of potential DG units. The capacities of DGs are considered in two ways with constant capacity and

with tuned capacity. These were for the best set of 1,3 and 5 DG units located within the 32 possible sites and

the corresponding Distribution system performance results.

Base case

PLOAD QLOAD PLOSSES QLOSSES PUTILITY QUTILIITY VSI

3.715MW 2.28MVAR 210.3KW 137.3KVAR 3.916MW 2.417MVAR 0.675

Table.2 Base System load flow Data

Bus Number Voltage

(p.u)

1 1.0000

2 0.9972

3 0.9836

4 0.9763

5 0.9691

6 0.9511

7 0.9475

8 0.9339

9 0.9276



10 0.9217

11 0.9208

12 0.9193

13 0.9132

14 0.9109

15 0.9095

16 0.9088

17 0.9067

18 0.9067

19 0.9061

20 0.9967

20 0.9931

21 0.9924

22 0.9918

23 0.9801

24 0.99734

25 0.9701

26 0.9491

27 0.9466

28 0.9351

29 0.9269

30 0.9234

31 0.9192

32 0.9183

33 0.9180

Table 2.1 Base case Voltage Profile

Fig. 3. Base case Voltage Profile Plot

Single DG Case The Following table represents the IVD, IVR values for best combination

IVD IVR

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

bus no.

volt

ag

e(p

er

unit

)



0.95499 0.95393

NPI Bus Number

PDG

(KW)

P Loss

(KW)

Q Loss

(KVAR)

VSI PUTILITY

(KW)

QUTLITY

(KVAR)

0.5489 31 1296.190 105.496 78.833 0.7971 2524.298 2378.832

0.5477 32 1244.690 105.718 79.501 0.7923 2576.028 2379.506

0.5411 33 1183.540 107.125 82.370 0.7865 2638.548 2382.369

0.5328 30 1471.350 108.178 80.041 0.8134 2351.828 2380.041

0.5331 6 1491.520 108.671 80.375 0.7923 13332.152 2380.376

Table 2.2. Single DG Test Result with 5 best combinations

Bus Number Voltage

(p.u)

1 1.0000

2 0.9988

3 0.9936

4 0.9925

5 0.9917

6 0.9877

7 0.9844

8 0.9713

9 0.9652

10 0.9596

11 0.9587

12 0.9573

13 0.9514

14 0.9492

15 0.9478

16 0.9472

17 0.9452

18 0.9446

19 0.9982

20 0.9947

21 0.9940

22 0.9933

23 0.9900

24 0.9834

25 0.9802

26 0.9875

27 0.9874

28 0.9852

29 0.9840

30 0.9848

31 0.9889

32 0.9881

33 0.9878

Table 2.3 Voltage Profile with Single DG at bus 31



Fig.4. Voltage Profile plot with Single DG at bus 31

3DG Case

The following table represents the IVD, IVR values for best combination.

IVD IVR

0.9675 0.95657

Without Tuning

DG sizes are 1000 KW, 750 KW, and 500 KW

NPI Bus

Number

DG

(KW) PDG

(Total)

(KW)

PLoss

(KW)

QLoss

(KVAR)

VSI PUTILIT

Y

(KW)

QUTILI

TY

(KVAR)

0.79106 31 14

25

1000 750

500

2250 69.339 51.6276 0.9176 1534.339 2351.62

3

0.78828

30

25

16

1000

750

500

2250 70.8966

9 57.962 0.9104 535.890

2351.96

2

0.78611

29

15

16

1000

750

500

2250 71.741 52.3996 0.89038 1536.743 2352.40

0

0.77022

11

31

4

1000

750

500

2250 75.8641 54.9118 0.89486 1540.665 2354.91

2

0.7644

30

9

25

1000

750

500

2250 77.088 55.8658 0.86670 1542.087 2355.86

6

Table 2.4. 3DG test Results without Tuning 5 best combinations.

0 5 10 15 20 25 30 350

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

bus no.

volt

ag

e(p

er

unit

)



With Tuning

NPI Bus

Numbe

r

DG

(KW) PDG

(Total)

(KW)

PLoss

(KW)

QLoss

(KVAR)

VSI PUTILIT

Y

(KW)

QUTI

LITY

(KVA

R)

0.79612

31

14 25

899.977

678.183 449.838

2027.69

9 68.4845 48.2754 0.9273 1755.786

2348.2

76

0.78903

30

25

16

955.671

724.049

480.026

2159.98 69.031 49.207 0.9138 1624.328 2349.0

21

0.78657

29

15

16

955.744

713.188

477.872

2146.80

5 70.181 49.479 0.9119 1638.377

2349.4

8

0.77024

11

31

4

980

731.25

490

201.25 72.1742 51.4284 0.8987 1585.924 2351.4

28

0.7625

30

9

25

931.994

706.110

465.997

2104.10

2 74.52 52.297 0.88297 18685.421

2352.2

98

Table 2.5. 3DG test Results with Tuning 5 best combinations.

Bus Number Voltage

(p.u)

1 1.0000

2 0.9995

3 0.9980

4 0.9974

5 0.9971

6 0.9942

7 0.9918

8 0.9871

9 0.9861

10 0.9874

11 0.9858

12 0.9861

13 0.9874

14 0.9879

15 0.9865

16 0.9859

17 0.9840

18 0.9835

19 0.9989

20 0.9954

21 0.9947

22 0.9940

23 0.9959

24 0.9922

25 0.9918

26 0.9936

27 0.9930

28 0.9889

29 0.9863

30 0.9862

31 0.9884



32 0.9876

33 0.9873

Table2.6 Voltage Profile with 3DGs at bus 31, 14, 25

5 DG Case

The Following table represents the IVD, IVR values for best combination.

IVD IVR

0.9949 0.9852

NPI Bus

Number PDG

(KW)

PDG

(Total)

(KW)

P Loss

(KW)

Q Loss

(KVAR)

VSI PUTILITY

(KW)

QUTLITY

(KVAR)

0.7877

24

18

33

8

9

1000

250

750

300

500

2800 74.9616 51.5129 0.91643 989.9644 2351.515

0.7847

32

25

2

15

12

1000

250

750

300

500

2800 75.2615 53.1826 0.91241 990.262 2335.184

0.7761

3 12

13

32

31

1000 250

750

300

500

2800 78.5738 78.5738 54.9075 0.89104 2354.902

0.7664

30

25

14

27

21

1000

250

750

300

500

2800 86.984 86.984 60.672 0.88.632 2360.675

Table. 2.7. 5DG Test Results without tuning 5 best combinations

NPI Bus

Number PDG

(KW)

PDG

(Total)

(KW)

P Loss

(KW)

Q Loss

(KVAR)

VSI PUTILITY

(KW)

QUTLITY

(KVAR)

0.79027

24

18

33

8

9

868.4808

222.6871

671.4525

264.5321

438.6601

2465.813 687794 47.6779 0.92094 1317.967 2374.678

0.78721

32 25

2

15

12

922.604 324.181

695.465

279.598

461.302

2593.151 71.703 50.63 0.91868 1193.553 2350.631

0.7772

3

12

13

32

31

940.3366

237.7294

709.5862

283.8345

477.8723

2650.359 71.9391 50.8451 0.908348 1136.580 2350.845

0.77432 30

25

821.808

206.4949 2287.264 72.488 51.145 0.91399 1500.224 2351.145



14 27

21

603.998 244.058

410.904

Table. 2.8. 5DG Test Results with tuning 5 best combinations

Table2.9 Voltage Profile with 3DGs at bus 24,18,33,8,9

In table 2, the base system load data, losses, voltage, voltage stability index and utility generated real

and reactive power are given. The base case voltage profile and corresponding voltage profile plot are in table 2.1 and fig.3 , the single DG case results are given for best five combinations in NPI priority order. This is the

case in which the user wants to connect single DG unit to utility in order to reduce loss and to improve voltage

profile and voltage stability index of the distribution system. The voltage improvement and voltage profile plot

with this case is shown in table 2.4 and fig.4.

In the table 2.4, the 3DG case results are given for best five best combinations in NPI priority order.

This is the case in which the user wants to connect 3DG with market available DG capacities to utility in order

to reduce loss and to improve voltage profile, voltage stability index and hence the NPI of the distribution

system from the previous single DG case. The voltage improvement and voltage profile plot with this case is

shown in table 2.6 and table 2.5, represents the 3DG case results for five best combinations with tuning of

market available DG sizes, which results reduction in losses, improvement in voltage stability index and hence

NPI of Distribution system from case of fixed DG capacities.

Bus Number Voltage

(p.u)

1 1.0000

2 1.0001

3 0.9989

4 0.9996

5 1.0007

6 1.0009

7 0.9985

8 0.9939

9 0.9929

10 0.9924

11 0.9925

12 0.9929

13 0.9941

14 0.9946

15 0.9933

16 0.9927

17 0.9908

18 0.9902

19 1.0001

20 1.0012

21 0.0018

22 1.0012

23 0.9961

24 0.9910

25 0.9891

26 1.0007

27 1.0006

28 0.9966

29 0.9940

30 0.9939

31 0.9900

32 0.9892

33 0.9889



Similarly in the 5DG case results are given for best five combinations are given in the tables 2.7 and

2.8 with market available DGs and with tuning of market available DGs. These cases results reduction in losses

improvement in voltage profile, voltage stability index of system and hence NPI.

VI CONCLUSION This work present a method of combining a new and fast power flow and genetic algorithms with an

aim to provide a means of finding the combination of sites within a distribution network for connecting a

predefined number of DGs. The network performance index is used in finding best combination of sites within

network. In doing so it evaluates the distribution system performance with DG capacities and maximizes the Network Performance index. Voltage stability index is used to determine the weak branches in the distribution

network. Its use world be to enable Distribution System Planner to search a network for the best sites to

strategically connect a small number of DGs among a large number of potential combinations in order to

improve Distribution system performance.

This work concentrated on the technical constraints on DG development like voltage limits, thermal

limits and especially the loss reduction (DG impacts on losses is an area that is being extensively researched at

present).

This work can be easily being adapted to cope with variable energy sources.

REFERENCES [1] H. B. Puttgen, P. R. Mac Gergor, F. C. Lambert, “Distributed generation: Semantic hype or the dawn of a new era?”, power &

Energy Magazine, IEEE, Vol.1, Issue. 1, pp.22-29, 2003

[2] W. EI-Khattam, M.M.A. Salama, “Distributed generation technologies definitions and benefits, Electric Power Systems Research,

vol.71, issue 2, pp. 119-128, October 2004.

[3] Thomas Ackermann, Goran Andersson, Lennart Soder, “Distributed generation: a definition”, Electric power system research, vol .

57 Issue 3, pp. 195-204-20 April 2001.

[4] J.L. Del Monaco, “The role of distributed generation in the critical electric power infrastructure”, power engineering society winter

meeting, 2001, IEEE, vol. 1, pp. 144-145,2001.

[5] Xu. Ding, A.A. Girgis, “Optimal load shedding strategy in power systems with distributed generation”, Power engineering Society

Winter Meeting, 2001, IEEE vol.2, pp, 788 – 793, 2001.

[6] N.S. Rau and Yih Heui Wan, “Optimum Location of resources in distributed planning”, IEEE Transactions on Power Systems, vol.

9, issue. 4, pp. 2014- 2020, 1994.

[7] J.O. Kim, S.W. Nam, S. K. Park, C. Singh, “ Dispersed generation planning using improved Hereford ranch algorithm”, Electric

Power Systems Research, vol. 47, issue 1, pp. 47-55, October 1998.

[8] T. Griffin, K. Tomsovic, D. Secrest, A. Law, “ Placement of dispersed generation systems for reduced losses”, Proceedings of the

33rd Annual Hawaii International Conference on System Science, 2000, IEEE, Jan 2000.

[9] K. Nara, Y. Hayashi, K. Ikeda, T. Ashizawa, “Application of tabu search to optimal placement of distributed generators”, Power

Engineering Society Winter Meeting, 2001, IEEE, vol. 2, pp.918 – 923, 2001.

[10] Caisheng Wang, M.H.Nehrir,, “ Analytical approaches for optimal placement of distributed generation sources in power systems “,

IEEE Transactions on power Systems, vol. 19, issue 4, pp.2068 – 2076, 2004.

[11] T. Gozel, M. H. Hocaoglu, U. Eminoglu, A. Balikci, “ Optimal placement and sizing of distributed generation on radial feeder with

different static load models”, international Conference on Future Power Systems, IEEE, pp. 1-6, issue. Now. 2005.

[12] Naresh Acharya, Pukar Mahat, N. Mithulananthan, “ An analytical approach for DG allocation in primary distribution network”,

Electrical Power and energy systems, vol.28 pp.669-678, feb.2006.




Depth Image Processing and Operator Imitation Using a Custom

Made Semi Humanoid

Ghanshyam Bhutra

1, Piyush Routray

2, Subrat Rath

3, Sankalp Mohanty

4

1(Dept. of EEE, S‘o’A University, India) 2(Dept. of E&I, S‘o’A University, India)

3(Dept. of EEE, S‘o’A University, India) 4(Dept. of EEE, S‘o’A University, India)

ABSTRACT : The motivation for creating humanoids arises from the diverse socio-economical interests

ranging from the restoration of day-to-day activities of differently abled to assisting humans in nearly

inaccessible areas such as mines, radiation sites, military projects, etc. Recent developments in the field of

image processing, thus enabling depth imaging and skeleton tracking easily has greatly increased the potential

of accurately inferring the signals of human operator. The time constraints on various jobs make them grossly dependant on real time data processing and execution. Also, the acceptance by the industrial community

depends on the accuracy of the complete system. The objective is to develop a proof based accurate system to

assist human operation in potentially inaccessible areas. The system has to analyze image feed from the camera

and deduce the gestures of the operator. Then the system communicates wirelessly with the self designed Semi-

Humanoid which in-turn, imitates the operator with maximum accuracy.

Keywords – Humanoid Robot, MATLAB, Microsoft Kinect, Servo Motor, Skeleton Tracking.

I. INTRODUCTION Humanoid robots, in the future, seem to be the best alternative for human labor. Communication at

work place environment greatly depends on body movements apart from direct audio signals. While verbal

signals give brief idea of the details of the work to be done, it is the physical demonstration that tends to have

maximum impact. Much work has progressively been done in the fields of gesture recognition and

implementation [1]-[3]. However, with advent of comparatively cheaper and easy to use depth imaging

techniques such as Microsoft Kinect, the accuracy of gesture recognition can be increased to greater percentage. Perfect imitation of the human, by a robot, requires tremendous calculations and harmonious work of many

different aspects of the system. A common form of mobile robot today is semi-autonomous, where the robot acts

partially on its own, but there is always a human in the control loop through a link i.e. Telerobotic. In this

technique, there are no sensors on the bot, those it may use to take a decision. One of the possible methods of

controlling such a bot is by amalgamation of depth imaging and skeletal tracking of the human operator.

For extending the area of application of the bot, it has to be wirelessly controlled and equipped with fine

maneuvering techniques. The signal transmission demands many-to-many communication with optimum use of

bandwidth and data protection. If achieved with maximum accuracy this technology stands to be of great

applications at outer space exploration, remote surgeries, hostile industrial and military conditions, security

purpose and of course, gaming and recreational activities.

II. APPROACH The approach to the problem was made on a calculated basis by first simulating on software and then

implementing as hardware. Broadly, the approach can be divided into four categories dealing with different

works. Successive amalgamations of the acquired results lead to the final goal. First step is to model the

Humanoid. It is followed designing of the control architecture, which is the suite of control required for

behavioral synchronization between human and the BOT. Next step caters to the acquisition and processing of

image feed from the Kinect sensor. Thus, the controlling parameters are determined here. Final step involves

wirelessly transmitting the inferred values of the controlling parameters to the Bot. the microcontrollers (mcu)

used in the robot decodes these parameters and thus redirects the robot to react accordingly. The approach has been discussed thoroughly through the following paragraphs.

Depth Image Processing and Operator Imitation Using a Custom Made Semi Humanoid


2.1 Hardware Modeling of the Humanoid

The software design was readied with 6 Degrees of Freedom. The model was divided into various links

and every link was modeled individually in the SolidWorks software. All the links were mated and ADAMS

was used to test the model under real physics by simultaneously solving equations for kinematics, statics, quasi-

statics and dynamics for multi-body system. Specifically for simulation purpose, the virtual model is designed

based on principles of kinematic modeling by Denavit-Hartenberg methodology for robotic manipulators [4].

There are total 18 Degrees of Freedom (DOF). Each arm has 5 DOF and each leg has 4 DOF. (Fig.1)

figure 1. Adam‟s plant model of humanoid for simulation.

The destination coordinates are fed into the ADAMS plant which has been configured to take input in

the form of angular velocities and output the current joint angles. The ADAMS plant is executed in MATLAB

(Simulink) environment with a function file to control it. The stability of the humanoid was inspired from

previous works done in this regard [5] – [8]. There are two separate methods to control the motion of a

humanoid i.e. DC (Direct Control) and CC (Command Control).Direct Control performs the conversion of 3D joint coordinates of the user to servo angles whereas the Command Control converts information to a single

command for the robot‟s understanding. The information that is sent is the destination positions for the tool

frame and the calculation of joint angles is left for the model. For gain of maneuvering options in terms of speed

and simplicity for most cases, the biped locomotion was voted against in favor of wheeled maneuvering system.

Next step was to construct the hardware model. The material for construction was chosen to be Aluminium and

6 Servo Motors of the rating 60g/17kg/0.14sec were used. For the base of the BOT, four motors, two each of the

rating 12V, 250 rpm, 60Kgcm and 12V, 250 rpm, 30Kgcm were used. To regulate the speed of the driving

motors, PWM was executed with Motor Drivers. The gross weight of the BOT is 10kg (approx.) including the

weight of the BECs (Battery Eliminator Circuits) and Power Supply. LiPo Batteries of the rating 11.1V, 6000

mAh 25~50C (Qty-2) and 11.1V, 850 mAh 20C (Qty-4) have been used for power supply. The servos operate in

the range 4.8V~6.0V and thus BECs were used to

convert 11.1V to 6.0V. The completed structure of the robot is shown as below. (Fig.2)

figure 2. Complete structure of the robot. Figure 3. Control architecture



2.2 Control Architecture

As shown above (fig.3), for convenience this has been described in two parts, AVR Controller and

ZIGBEE Communication. The AVR microcontroller that has been used is ATMEGA 32 which is an 8-bit MCU.

This controls the servo motors and the driving motors. There are in-total 10 motors to be controlled (6 Servo + 4

Shunt). Four MCUs have been used, out of which three control the servos and the remaining one controls the

driving motors. Each MCU has been interfaced with an LCD to continuously display its status.

ZIGBEE is the standard that defines a set of communication protocols for low-data-rate, very low power

applications. We are using XBee RF modules for communication. These modules interface to a host device

through a logic-level asynchronous serial port. The link between XBee and ATMEGA 32 takes place via UART

(Universal Asynchronous Receiver/Transmitter). The data format and transmission speeds are configurable. The

AVR CPU connects to AVR UART via six registers i.e. UDR, UCSRA, UCSRB, UCSRC, UBRRH and

UBRRL.

2.3 Depth Image Processing & Gesture Interpretation.

Depth Imaging is the central point of this publication as it can be considered a giant leap in terms of

boosting accuracy while operator imitation. The sensor being used is Kinect which is a motion sensing input

device by Microsoft for XBOX 360 video game console. It is a horizontal bar connected to a small base with a

motorized pivot and is designed to be positioned lengthwise. The device features an "RGB camera, depth sensor

and multi-array microphone running proprietary software", which provide full-body 3D motion capture, facial

recognition and voice recognition capabilities. The depth sensor consists of an infrared laser projector combined

with a monochrome CMOS sensor, which captures video data in 3D under any ambient light conditions. The

sensing range of the depth sensor is adjustable, and the Kinect software is capable of automatically calibrating

the sensor based on the physical environment, accommodating for the presence of the user.

Figure 4. Kinect Sensor.

Third party software and open source drivers were implemented to gain compatibility with MATLAB. At the heart of Kinect, lies a time of flight camera that measures the distance of any given point from the sensor using

the time taken by near-IR light to reflect from the object. In addition to it, an IR grid is projected across the

scene to obtain deformation information of the grid to model surface curvature. Cues from RGB and depth

stream from the sensor are used to fit a stick skeleton model to the human body. The reference frame for the

Kinect sensor is a predefined location approximately at the center of the view. The horizontal right hand side of

observation denotes the x axis and transverse axis denotes y axis

The z axis is the linear distance between the Kinect and user, the farther the user the higher the z value. We

monitor the real time positions and orientations of 15 different data points in the user‟s body, namely: Head,

Neck, Left Shoulder, Left Elbow, Left Hand, Right Shoulder, Right Elbow, Right Hand, Torso, Left Hip, Left

Knee, Left Foot, Right Hip, Right Knee and Right Foot. The sensor generates a 2D Matrix of 225x7 dimension

with the above mentioned datapoints for a single person. The seven columns are User ID, Tracking Confidence, x, y,z (coordinates in mm) and the next X,Y (the pixel value of the corresponding data point). Using the raw

data points from Kinect, vectors are constructed and using those, the angles between different links and the

lengths of different links were found. The skeletal map is obtained according to user specisified link and node

colours as shown in fig.5a&b.



Figure5 a. Skeletal mapping in low light conditions.

Figure5 b. Skeletal mapping in sufficient lighting.

The data points are then used to calculate and derive values ranging from link length to angular velocity as

discussed in the later part.

2.4 Data transmission and Robot Control

As mentioned earlier, the technology defined by the ZigBee specification is intended to be simpler and

less expensive than other WPANs, such as Bluetooth. ZigBee is targeted at radio-frequency (RF) applications

that require a low data rate, long battery life, and secure networking. One of the Xbee pair is connected to the

PC using Xbee USB adapter board and the other one is interfaced to the Microcontroller. The data enters the module‟s UART from the serial port of the PC through the DI pin as ansynchronous serial signal. The signal

should idle be high when no data is being transmitted. Each data byte consists of a start bit(low), 8 databits

(least significant being first) and a stop bit.

Now that the information to be sent and method of communication is ready, the next step is finding out the

memory addressing technique so that the data transmitted should be in synchronization with the data received.

In order to address the 10 actuated joints with a single duplex channel of 8 bit word length we had to involve an

addressing strategy. The number of bits required to address 10 individual motors would ideally be 4 bits and to

transmit 8 bits of data to each of the motors makes the word length of 12 bits which is not available. So instead

of delivering the word in a single transmission the information is broken in 2 nibbles of 4 bit length and 2

consecutive transmissions are required to successfully transmit the 8 bit of data. With this split we had to assign

1 bit to distinguish between the higher and the lower nibble which again created a problem as the word length of

a transmission is limited to 8 bits only. Two separate 4-bit address for each motor are used to send the higher and lower nibble of data from the source. This scheme allows us to address a 40 maximum of 256 different

addressees and transmit information of 8 bits to each one in 2 consecutive transmission. The analogy, for

betterunderstanding can be presented as in fig.6.

Figure 6. Diagramatic analogy of communication method.

After the reception of the control signal at the remotely located manipulators the microcontrollers

controlling the servos decodes the servo angles from the control signal and implements it. Controlling a servo

using a microcontroller requires no external driver like H-bridge only a control signal needs to be generated for

the servo to position it in any fixed angle. The control signal is 50Hz (i.e. the period is 20ms) and the width of

positive pulse controls the angle. This implementation of the servo angle completes 1 cycle of the process as it

ends with the beginning of the kinect sensor inputting the joint information to the control system. Thus, in this

way the behavioral synchronization between an operator and the BOT is achieved.



III. IMPLEMENTATION The dynamic process of behavioral synchronization through a humanoid begins with derivation of real

time information about the joints of the user be it prismatic (linear) or revolute (rotational). With degree of

freedom as high as 10 the task becomes extremely tedious and requires tremendous processing power. One of

the major problems in monitoring multiple joints information is the definition of the reference frame with

respect to which information about the other frames can be derived. Even though the reference for individual

joints may vary it is convenient to process all the joints with a single reference point. The Kinect Sensor

provides this information to the central control block using a combination of infrared imaging and RGB camera.

The infrared laser creates a depth profile of the area in display and the RGB camera returns RGB matrix. The sensor generates a 2D matrix of 225 x 7 dimension with the above mentioned data points for a single person

and a maximum tracking capability of 15 people. The seven columns are User ID, Tracking Confidence, x, y, z

(coordinates in mm) and the next X, Y (the pixel value of the corresponding data point). Using this information

we calculate the joint angles in different planes between different links as well as the link lengths between data

points.

Having raw joint information is not enough for further operations, because of that on the next step the

vector between joints should be constructed.

As an example below a description of construction of vector between joints of right hand has been given:

Connecting the right shoulder, right elbow and right elbow and palm determines the angle for right shoulder.

Right shoulder = 𝑥𝑟𝑠 ,𝑦𝑟𝑠 , 𝑧𝑟𝑠

Right elbow = 𝑥𝑟𝑒 ,𝑦𝑟𝑒 , 𝑧𝑟𝑒

Right palm = 𝑥𝑟𝑝 , 𝑦𝑟𝑝 , 𝑧𝑟𝑝

Vect1 = 𝑥𝑟𝑠 ,𝑦𝑟𝑠 , 𝑧𝑟𝑠 − 𝑥𝑟𝑒 ,𝑦𝑟𝑒 , 𝑧𝑟𝑒

Vect2 = 𝑥𝑟𝑝 , 𝑦𝑟𝑝 , 𝑧𝑟𝑝 − 𝑥𝑟𝑒 ,𝑦𝑟𝑒 , 𝑧𝑟𝑒

Now the angle between the vectors is calculated by using a simple geometrical operation such as angle between

two vectors.

Joint angle = cos−1 𝑉𝑒𝑐𝑡 1 ∙𝑉𝑒𝑐𝑡 2

𝑉𝑒𝑐𝑡 1 ∗ 𝑉𝑒𝑐𝑡 2

Using the x, y and z coordinates derived from Kinect sensor and using the formula for distance calculation the

link lengths are found.

length = (xrs − xre )2 + (yrs − yre )2 + (zrs − zre )2

After calculating the joint angles and link lengths, the next step in the behavioral synchronization would be to

realize these real time parameters in the desired form. This step includes conversion of the parameters to actual

servo angles. It is necessary for tuning purpose between the real angle values of joints and robot servo values.

Another reason for the necessity of this step is because the real human joint angle values range from 0~360°,

when the robot servo motor can accept values from 0~255.

Now that the destination coordinates of the individual tool frames for the humanoid robot have been derived,

two simultaneous steps follow, embedding the gesture movements into the virtual (software) and real (hardware)

model.

Since each arm has 3DOF, the tool frame coordinates – x, y and z depends on 3 angles θ1 , θ2 and θ3 which can

be expressed as:

xyz = T

θ1

θ2

θ3

Here „T‟ is the transformation matrix that relates the coordinates with the joint angles.



xy z

= J

θ1

θ2

θ3

„J‟ denotes the Jacobian matrix and x , y and z are the product of error signal of the corresponding axis and the

proportionality constantkp andθ1 , θ2

and θ3 are the joint anglular velocities.

x = (xdestination − x) × kp

y = (ydestination − y) × kp

z = (zdestination − z) × kp

To evaluate the angular velocities, we use the expression,

θ1

θ2

θ3

= J−1 xy z

These angular velocities are then converted as discussed earlier and then transmitted to the Robot. The servo-

motors attached at designated joints thus move accordingly and operator imitation is achieved.

Figure7. Imitation of its human operator by ACE, the semi-humanoid robot.

IV. CONCLUSION The implemented architecture suggests simple, low cost and robust solution for controlling a semi-

humanoid with human gestures. The complete setup requires the synchronization of software and hardware. The

above mentioned problems were faced for setting it up. This project can serve as a development platform for

interaction between human and BOT via gestures and with the quality of resources of products, more

sophistication can be inculcated. Presently biped motion and holding mechanism in the hands are being worked

upon. The legged motion is expected to enable the robot to maneuver over uneven terrains easily than the

wheels. The picking/holding mechanism would increase its applicability in the industries by manifolds. Thus the greater goal of attaining better man-machine teams for dynamic environments can be realized with perfection.

ACKNOWLEDGEMENTS We acknowledge the research facilities extended to us at Centre for Artificial Intelligence and Robotics

(DRDO), Bengaluru for carrying out the simulations and learning other mechanical nuances of humanoids.

We also thank faculty advisor of the ITER Robotics Club, Asst. Prof Farida A Ali for her continuous guidance

and support over the period of research.

http://www.irc.myiter.co.in/



REFERENCES [1] David Koterkamp, Eric Huber and R. Peter Bonasso, “Recognising and Interpreting Gestures on a Mobile Robot”, Thirteenth

National Conference on Artificial Intelligence (AAAI), 1996.

[2] Stefan Waldherr, Roseli Romero and Sebastian Thrun, “A Gesture Based Interface for Human-Robot Interaction”, Kluwer

Academic Publishers, 2000.

[3] Hideaki Kuzuoka, Shin'ya Oyama, Keiichi Yamazaki, Kenji Suzuki and Mamoru Mitsuishi, “GestureMan: A Mobile Robot that

Embodies a Remote Instructor's Actions”, Proceedings of SCW‟00, December 2-6, 2000.

[4] John J. Craig, Introduction to Robotics, pg70-74, 3rd

Edition, Pearson Education International. 2005.

[5] Vitor M. F. Santos and Filipe M. T. Silva, “Engineering Solutions to Build an Inexpensive Humanoid Robot Based on a Distributed

Control Architecture”, 5thIEEE-RAS InternationalConference on Humanoid Robots, 2005.

[6] “Engineering Solutions to Build an Inexpensive Humanoid Robot Based on a Distributed Control Architecture”, 5thIEEE-RAS

InternationalConference on Humanoid Robots, 2005.

[7] Santos, F. Silva, “Development of a Low Cost Humanoid Robot: Components and Technological Solutions”, in Proc. 8th

International Conference on Climbing and Walking Robots, CLAWAR‟2005, London, UK, 2005.

[8] J.-H. Kim et al. –“Humanoid Robot HanSaRam: Recent Progress and Developments”, J. of Comp. Intelligence, Vol 8, nº1, pp.45-

55, 2004.

[9] Jung-Hoon Kim and Jun-Ho Oh, “Realization of dynamic walking for the humanoid robot platform KHR-1”, Advanced Robotics,

Vol. 18, No. 7, pp. 749–768 (2004)

[10] K. Hirai et al., “The Development of Honda Humanoid Robot”, Proc. IEEE Int. Conf. on R&A, pp. 1321-1326, 1998.

[11] [Kinect] Use With Matlab www.timzaman.nl

[12] KINECTHACKS www.kinecthacks.com




DVR Based Compensation of Voltage Sag due to Variations of

Load: A Study on Analysis of Active Power

Anita Pakharia1, Manoj Gupta

2

Department of Electrical Engineering 1Assistant Professor, Global College of Technology, Jaipur, Rajasthan, India

2Professor, Poornima College of Engineering, Jaipur, Rajasthan, India

ABSTRACT : Dynamic Voltage Restorer (DVR) has become very popular in recent years for compensation

of voltage sag and swell. The voltage sag and swell is very severe problem of power quality for an industrial customer which needs urgent attention for its compensation. There are various methods for the compensation of

voltage sag and swell. One of the most popular methods of sag and swell compensation is Dynamic Voltage

Restorer (DVR), which is used in both low voltage and medium voltage applications. In this work, our main

focus is on DVR. DVR compensate the voltage sag by injecting voltage as well as power into the system. The

compensation capability of this is mainly influenced by the various load conditions and voltage dip to be

compensated. In this work the Dynamic Voltage Restorer is designed and simulated with the help of Matlab

Simulink for sag compensation. Efficient control technique (Park’s Transformations) is used for mitigation of

voltage sag through which optimized performance of DVR is obtained. The performance of DVR is analyzed on

various conditions of active and reactive power of load at a particular level of dc energy storage. Parameters of

load are varied and the results are analyzed on the basis of output voltages.

Keywords —Structure and control technique for dvr, dvr test system, power quality.

I. INTRODUCTION Power quality (PQ) issue has attained considerable attention in the last decade due to large penetration

of power electronics based loads and microprocessor based controlled loads. On one hand these devices

introduce power quality problem and on other hand these mal-operate due to the induced power quality problems. PQ disturbances cover a broad frequency range with significantly different magnitude variations and

can be non-stationary, thus, appropriate techniques are required to compensate these events/disturbances [1].

The growing concern for power quality has led to development for variety of devices designed for mitigating

power disturbances, primarily voltage sag and swell. Voltage sag and swell are most wide spread power quality

issue affecting distribution systems, especially industries, where involved losses can reach very high values.

Short and shallow voltage sag can produce dropout of a whole industry [2]. In general, it is possible to consider

voltage sag and swell as the origin of 10 to 90% power quality problems. The main causes of voltage sag are

faults and short circuits, lightning strokes, and inrush currents and swell can occur due to a single line-to ground

fault on the system and also be generated by sudden load decreases, which can result in a temporary voltage rise

on the unfaulted phases [1] [2].

The voltage sag and swell is very severe problem for an industrial customer which needs urgent

attention for its compensation. Among several devices, a Dynamic Voltage Restorer (DVR) is a novel custom

power device proposed to compensate for voltage disturbances in a distribution system. The dynamic voltage

restorer is the most efficient and effective power device used in power distribution networks. Its appeal includes

lower cost, smaller size, and its fast dynamic response to the disturbance. Our main focus in this thesis is on the

Dynamic Voltage Restorer (DVR) [3]. This device using series-connected VSC is used to inject controlled

voltage (controlled amplitude and phase angle) between the Point of Common Coupling (PCC) and the load [3]

[4].

For proper voltage sag and swell compensation, it is necessary to derive suitable and fast control scheme for inverter switching. Here, we develop the simulation model using MATLAB SIMULINK and also discusses

simulation results with different load conditions.

II. STRUCTURE AND CONTROL TECHNIQUE FOR DVR DVR is connected in the utility primary distribution feeder. This location of DVR mitigates the certain

group of customer by faults on the adjacent feeder as shown in fig. 1. The point of common coupling (PCC) feds

DVR Based Compensation of Voltage Sag due to Variations of Load: A Study on Analysis of Active

Power


the load and the fault. The voltage sag in the system is calculated by using voltage divider rule [5]. The general

configuration of the DVR consists of:

(a) Series injection transformer (b) Energy storage unit

(c) Inverter circuit (d) Filter unit

(e) DC charging circuit (f) A Control and Protection system

Energy storage device is the most expensive component of the DVR, therefore it is essential

requirement to use such mitigation strategy at which DVR can operate with minimum energy storage

requirement. Different voltage sag mitigation strategies including pre-sag, in-phase, and phase advance

compensation have described in this thesis. Injection of active power by DVR is related to energy storage. DVR

injecting large amount of active power requires bigger size of energy storage leading to more expensive scheme.

Therefore, optimization of energy storage can be obtained by optimizing DVR active power injection. In case of

zero active power injection by DVR, it injects reactive power only to compensate for voltage sag [6] [7].

AC SourceStep-down

Transformer

LOAD1

DVR

Step-down

Transformer

Step-down

Transformer

Sensitive

Load

Transmission Line Distribution Line

Fig. 1 Location of DVR

Control of DVR is performed by using d-q coordinate system. This transformation allows DC components,

which is much simpler than AC components.

The dqo transformation or Park’s transformation is used to control of DVR. The dqo method gives the sag depth

and phase shift information with start and end times. The quantities are expressed as the instantaneous space

vectors. Firstly convert the voltage from a-b-c reference frame to d-q-o reference [8].

Vd

Vq

Vo

=

cos θ cos θ −2π

3 1

−sin(θ) −sin θ −2π

3 1

1

2

1

2

1

2

Va

Vb

Vc

(1)

Above equation 1 defines the transformation from three phase system a, b, c to dqo stationary frame. In this

transformation, phase A is aligned to the d axis that is in quadrature with the q-axis [9]. The theta (θ) is defined

by the angle between phases A to the d-axis. The error signal is used as a modulation signal that allows

generating a commutation pattern for the power switches (IGBT’s) constituting the voltage source converter.

The commutation pattern is generated by means of the sinusoidal pulse width modulation (SPWM) technique, voltages are controlled through the modulation [8] [9].

III. DVR TEST SYSTEM Electrical circuit model of DVR test system is shown in fig.2. System parameters are listed in table 1.

Voltage sag is created at load terminals via a three-phase fault. Load voltage is sensed and passed through a

sequence analyzer [10]. The magnitude is compared with reference voltage. MATLAB Simulation model of the

DVR is shown in fig.3 which is display at the end if this paper. Table 1 shows the values of system parameters.

System comprises of 15 kV, 50 Hz generator, feeding transmission lines through a 3-winding transformer

connected in Y/Δ/Δ, 15/115/11 [10] [11].


Power


VDC

Storage

VS

Source

load

Series Injection

Transformer

Three Phase Fault 'X'

Three PhaseV-I measurement

A

B

C

IGBTInverter

G

Pulse PLL

+

_

Vref

Constant

Positive Sequence

Magnitude

Bus p.u.

Voltage

1

Vin

Fig. 2 Circuit Model of DVR Test System

TABLE 1 SYSTEM PARAMETERS

S.No. System Quantities Ratings

1. Main Supply

Voltage Per Phase 15 KV

2. Line Impedance Ls = 0 .006 H , Rs=0.002Ω

3. Series Transformer

Turns Ratio 1:1

4. Filter Inductance 1mh

5. Filter Capacitance 0.50 µF

6. Load Resistance 40 Ω

7. Load Inductance 0.05 H

8. Line Frequency 50 HZ

9. Inverter

Specifications

IGBT Based, 3 Arms, 12

Pulse,

Carrier Frequency=1024 HZ

Sample Time= 0.5 sec.

Here, the outputs of a three-phase half-bridge inverter are connected to the utility supply series

transformer. Once a voltage disturbance occurs, with the aid of dqo transformation based control scheme (Park’s

Transformation), the inverter output can be steered in phase with the incoming ac source while the load is

maintained constant. As for the filtering scheme of the proposed method, output of inverter is installed with

capacitors and inductors [11] [12].

IV. SIMULATION RESULTS Dynamic Voltage Restorer is simulated using MATLAB SIMULINK and the results are analyzed on

the basis of output voltage. Various cases of different active power of load at different dc energy storage are


Power


considered to study the impact on sag waveform and compensated waveform as shown in fig. 4 to 15. These

various cases are listed in table 2 and discussed below.

Case I : A three-phase fault is created via a fault resistance of 0.55 Ω, load 1 is 5 KW, 100 VAR and load 2 is

10 KW, 100 VAR which results in a voltage sag of 10.07 %. Transition time for the fault is considered from 0.1

sec to 0.14 sec as shown in fig.4. Fig.5 and 6 shows the voltage injected by the DVR and the corresponding load

voltage. The simulation results and DVR performance in presence of dc energy storage reveals that 99.43 % of

sag is compensated and deviation of 0.57 % is attained from three phase source voltage with 600 V of dc energy storage

(a)

(b)

Fig. 4 Three phase voltage sag at load 5 KW, 100 VAR:

(a) Source voltage (b) RMS value of source voltage

(a)

(b)

Fig. 5 Three phase injected voltage at load 5 KW, 100 VAR:

(a) Injected voltage (b) RMS value of injected voltage

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

Volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)


Power


(a)

(b)

Fig. 6 Three Phase Compensated Voltage at load 5 KW, 100 VAR:

(a) Load (Improved) Voltage (b) RMS Value of Load (Improved) Voltage

Case II : A three-phase fault is created via a fault resistance of 0.55 Ω, load 1 is 10 KW, 100 VAR and load 2 is

10 KW, 100 VAR which results in a voltage sag of 28 %. Transition time for the fault is considered from 0.1 sec

to 0.14 sec as shown in fig.7. Fig.8 and 9 shows the voltage injected by the DVR and the corresponding load

voltage. The simulation results and DVR performance in presence of dc energy storage reveals that 98.46 % of

sag is compensated and deviation of 1.54 % is attained from three phase source voltage with 3.1 KV of dc

energy storage.

(a)

(b)

Fig. 7. Three phase voltage sag at load10 KW, 100 VAR


0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Tim

e (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)


Power


(a)

(b)

Fig. 8. Three phase injected voltage at load 10 KW, 100 VAR:


(a)

(b)

Fig. 9. Three phase compensated voltage at load 10 KW, 100 VAR:

(a) Load (improved) voltage (b) RMS value of load (improved) voltage

Case III : A three-phase fault is created via a fault resistance of 0.55 Ω, load 1 is 50 KW, 100 VAR and load 2

is 10 KW, 100 VAR which results in a voltage sag of 80 %. Transition time for the fault is considered from 0.1

sec to 0.14 sec as shown in fig. 10. Fig.11 and 12 shows the voltage injected by the DVR and the corresponding

load voltage. The simulation results and DVR performance in presence of dc energy storage reveals that 97.96

% of sag is compensated and deviation of 2.03 % is attained from three phase source voltage with 8.5 KV of dc

energy storage.

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)


Power


(a)

(b)

Fig. 10. Three phase voltage sag at load 50 KW, 100 VAR:


(a)

(b)

Fig. 11. Three phase injected voltage at load 50 KW, 100 VAR:


(a)

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2 0.3-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.2 0.30

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2 0.3-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)


Power


(b)

Fig. 5.12. Three phase compensated voltage at load 50 KW, 100 VAR: (a) Load (improved) voltage (b) RMS value of load (improved) voltage

Case IV : A three-phase fault is created via a fault resistance of 0.55 Ω, load 1 is 1 MW, 100 VAR and load 2 is

10 KW, 100 VAR which results in a voltage sag of 97.12 %. Transition time for the fault is considered from 0.1

sec to 0.14 sec as shown in fig.13. Fig.14 and 15 shows the voltage injected by the DVR and the corresponding load voltage. The simulation results and DVR performance in presence of dc energy storage reveals that 99.39

% of sag is compensated and deviation of 0.61 % is attained from three phase source voltage with 13 KV of dc

energy storage.

(a)

(b)

Fig. 5.13. Three phase voltage sag at load 1MW, 100 VAR:


(a)

0 0.1 0.2 0.30

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.20

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2 0.3-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)


Power


(b)

Fig. 5.14. Three phase injected voltage at load 1 MW, 100 VAR: (a) Injected voltage (b) RMS value of injected voltage

(a)

(b)

Fig. 5.15. Three phase compensated voltage at load 1 MW, 100 VAR:

(a) Load (improved) voltage (b) RMS value of load (improved) voltage

TABLE 2 VARIATIONS WITH PARAMETERS OF ACTIVE POWER OF LOAD

Case Load 1 Load 2 Fault

Resistance Sag

DC Energy

Storage

Compensated

Voltage

Devi

ation

I 5 KW, 100 VAR 10 KW, 100 VAR 0.55 Ω 10.07 % 600 V 99.43 % 0.57 %

II 10 KW, 100

VAR 10 KW, 100 VAR 0.55 Ω 28 % 3.1 KV 98.46 %

1.54 %

III 50 KW, 100

VAR 10 KW, 100 VAR 0.55 Ω 80 % 8.5 KV 97.97 %

2.03 %

IV 1 MW, 100

VAR 10 KW, 100 VAR 0.55 Ω 97.12 % 13 KV 99.39 % 0.61

Hence, simulation results shows that by increasing the dc storage the effect of voltage sag in output voltage is

decreased. As can be seen from table 2 with increment in load 1 while keeping load 2 constant the voltage sag

continuously increases which can be compensated by proportional increase in the dc energy storage.

0 0.1 0.2 0.30

5000

10000

15000

Time (sec)

Voltage (

volts)

0 0.1 0.2 0.3-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time (sec)

Voltage (

volts)

0 0.1 0.2 0.30

5000

10000

15000

Time (sec)

Voltage (

volts)


Power


V. CONCLUSION Hence foregoing analysis shows the impact of DVR and dc energy storage on voltage sag

compensation. Consideration on different active power of load with varying dc energy storage shows that with

increasing values of load there is an enhancement of voltage sag which can be further compensated by adjusting

the values of dc energy storage. The simulation results are compared on the basis of output voltages and output

waveforms shows that DVR is fairly efficient for compensation of sag and swell. Further dimensions to this work can be added by analyzing other disturbances of power quality like voltage

swell, harmonics etc. Various sag compensations cases are discussed while varying the active power of load.

Fig.4. Simulation model of the DVR

REFERENCES [1] Mladen Kezunovic, ―A Novel Software Implementation Concept for Power Quality Study‖, IEEE Transactions on power delivery,

vol. 17, NO. 2, April 2002.

[2] Boris Bizjak, Peter Planinšič, ―Classification of Power Disturbances using Fuzzy Logic‖, University of Maribor - FERI, Smetanova

17, Maribor, SI – 2000.

[3] J. G. Nielsen, M. Newman, H. Nielsen, and F. Blaabjerg, ―Control and Testing of DVR at Medium Voltage‖, IEEE Transaction on

Power Electronics, Vol.19.No.3, May 2004.

[4] Burke J.J., Grifith D.C., and Ward J., ―Power quality—Two different perspectives‖, IEEE Trans. on Power Delivery, vol. 5, pp.

1501–1513, 1990.

[5] Margo P., M. Heri P., M. Ashari, Hendrik M. and T. Hiyama, ―Compensation of Balanced and Unbalanced Voltage Sags using

Dynamic Voltage Restorer Based on Fuzzy Polar Control‖, International Journal of Applied Engineering Research, ISSN 0973-

4562 Volume 3, Number 3, pp. 879–890, 2008.

[6] Y. W. Lie, F. Blaabjerg, D. M. Vilathgamuwa, P. C. Loh, ―Design and Comparison of High Performance Stationary-Frame

Controllers for DVR Implementation‖, IEEE Transactions on Power Electronics, Vol.22, No.2, March 2007.

sag compansator

powergui

injectedwave1

scopedata9

Injectedwave

scopedata8

PWM

scopedata7

wave1

scopedata6

improvedwave1

scopedata5

sag1

scopedata4

wave

scopedata3

improvedwave

scopedata2

sag

scopedata1

Continuous

dq0

sin_cosabc

dq0_to_abc

Transformation

abc

sin_cos

dq0

abc_to_dq0

Transformation1

abc

sin_cosdq0

abc_to_dq0

TransformationTransport

Delay

A1+

A1

B1+

B1

C1

+

C1

A2+

A2

B2+

B2

C2

+

C2

Three-Phase Transformer

12 Terminals

A

B

C

Three-Phase Source

A

B

C

a

b

c

Three-Phase Breaker

VabcIabc

A

B

C

abc

Three-Phase

V-I Measurement5

VabcA

B

C

a

b

c

Three-Phase

V-I Measurement4

VabcIabc

A

B

C

abc

Three-Phase

V-I Measurement3

Vabc

IabcA

B

C

a

b

c

Three-Phase

V-I Measurement2

VabcIabc

A

B

C

abc

Three-Phase

V-I Measurement

A

B

C

a

b

c

Three-Phase

Transformer

(Two Windings)

A

B

C

Three-Phase

Series RLC Load1

A B C

A B CThree-Phase

Series RLC Branch1

A

B

C

A

B

C

N

A

B

C

Three-Phase

Programmable

Voltage Source

A

B

C

Three-Phase

Parallel RLC Load 3

A B C

Three-Phase

Parallel RLC Load 2

A

B

C

A

B

C

Three-Phase

PI Section Line1

g

A

B

C

+

N

-

Three-Level Bridge

ScopesagrmsScopesag

Scopeload

Scope9

Scope8

Scope7

Scope6

Scope5

Scope2Scope12

Scope10

RMS

RMSsag

RMS

RMSload

RMS

RMS2

RMS

RMS1

Signal(s)Pulses

PWM Generator

node 10

node 10

Divide

Vabc (pu)

Freq

wt

Sin_Cos

Discrete

3-phase PLL

DC Voltage Source1

DC Voltage Source

1

Constant


Power


[7] P.W. Lehn and MR. Iravani, ―Experimental Evaluation of STATCOM Closed Loop Dynamics‖, IEEE Trans. Power Delivery, vol.

13, no.4, pp.1378-1384 October 1998.

[8] Rosli Omar, Nasrudin Abd Rahim, Marizan Sulaiman, ―Modeling and 8simulation for voltage sags/swells mitigation using dynamic

voltage restorer (dvr)‖, Journal of Theoretical and Applied Information Technology, JATIT, 2005 - 2009.

[9] S. Chen, G. Joos, L. Lopes, and W. Guo, ―A nonlinear control method of dynamic voltage restorers‖, IEEE 33rd Annual Power

Electronics Specialists Conference, pp. 88- 93, 2002.

[10] H.P. Tiwari, Sunil Kumar Gupta, ―Dynamic Voltage Restorer Based on Load Condition‖, International Journal of Innovation,

Management and Technology, Vol. 1, No. 1, ISSN: 2010-0248, April 2010.

[11] Toni Wunderline and Peter Dhler, ―Power Supply Quality Improvement with A Dynamic Voltage Restorer (DVR)‖, IEEE

Transaction, 1998.

[12] Carl N.M.Ho, Henery and S.H. Chaung, ―Fast Dynamic Control Scheme for Capacitor- Supported Dynamic Voltage Restorer:

Design Issues, Implementation and Analysis‖, IEEE Transaction, 2007.

1Anita Pakharia obtained her B.E. (Electrical Engineering) in year 2005 and pursuing M. Tech. in Power systems

(batch 2009) from Poornima College of Engineering, Jaipur. She is presently Assistant Professor in the Department of

Electrical Engineering at the Global College of Technology, Jaipur. She has more than 6 years of teaching experience.

She has published five papers in National/ International Conferences. Her field of interest includes Power Systems,

Generation of electrical power, Power electronics, Drives and Non-conventional energy sources etc.

2 Manoj Gupta received B.E. (Electrical) and M. Tech. degree from Malaviya National Institute of Technology

(MNIT), Jaipur, India in 1996 and 2006 respectively. In 1997, he joined as Electrical Engineer in Pyrites, Phosphates

and chemicals Ltd. (A Govt. of India Undertaking), Sikar, Rajasthan. In 2001, he joined as Lecturer in Department of

Electrical Engineering, Poornima College of Engineering, Jaipur, India and now working as Associate Professor in

Department of Electrical Engineering in the same. His field of interest includes power quality, signal/ image

processing, electrical machines and drives. Mr. Gupta is a life member of Indian Society for Technical Education

(ISTE), and Indian Society of Lighting Engineers (ISLE).




Performance Analysis of Coded OFDM Signal for Radio over

Fiber Transmission

Shikha Mahajan1, Naresh Kumar

2

1(ECE, U.I.E.T/ Panjab University, India) 2(Assistant Professor, ECE, U.I.E.T/Panjab University, India)

ABSTRACT : The main aim of this paper is to analyze the influence of the modulation techniques; on the

performance of coded orthogonal frequency-division multiplexing (COFDM) based Radio over Fiber (RoF)

system. The modelling of the COFDM scheme for RoF system has been completely done by using software

Optisystem. Both analysis and simulation results are presented for 16-QAM and 16-QPSK modulation

techniques and performance is analyzed for optical amplifier and Erribium Doped Fiber Amplifier (EDFA).

The parameter Quality factor (Q) of the COFDM signal transmitted through the fiber optic link is examined.

From the analysis for Radio over Multimode Fiber with link length of 2 km (medium haul communication)

optical amplifier gives better results than EDFA for either of the modulation schemes for set of bit rates.

Keywords – COFDM, Convolutional Encoder, Multimode Fiber, Quality Factor, Radio over Fiber

I. INTRODUCTION RoF technology is one of the recent advancements in optical communication, finding applications in

cable TV networks, base station links for mobile communication and antenna remoting [1]. Particularly, radio

over multimode fiber system (ROMMF) has gained much attention recently for its suitability to deliver high-

purity signals in analog and digital format for short-reach coverage such as wireless local area networks and ultrawideband radio signals [2].

COFDM has been specified for digital broadcasting systems for both Digital Audio Broadcasting

(DAB), Digital Video Broadcasting (DVB-T) [3]. COFDM is particularly well matched to these applications,

since it is very tolerant of the effects of multipath (provided a suitable guard interval is used). Indeed, it is not

limited to 'natural' multipath as it can also be used in so-called Single-Frequency Networks (SFNs) in which all

transmitters radiate the same signal on the same frequency. A receiver may thus receive signals from several

transmitters, normally with different delays and thus forming a kind of 'unnatural' additional multipath.

Provided the range of delays of the multipath (natural or 'unnatural') does not exceed the designed tolerance of

the system (slightly greater than the guard interval) all the received-signal components contribute usefully. For

short-distance applications, COFDM can be used efficiently for high data rate signal delivery and to tolerate the

effect of modal dispersion. Also, the exponent refractive index profile effect has been studied and shown that

the best performance can be achieved near the optimum value of exponent refractive index profile [4]. COFDM system employs conventional forward error correction codes and interleaving for protection

against burst errors caused by deep fades in the channel. Often it is concatenated with a block code for

improving the performance [5]. A Viterbi decoder for decoding a convolution code is easy to implement. Codes

of different rates are used in conjunction with different modulation schemes to support different quality of

services. While error correction codes provide coding gain in the system, interleaving provides diversity gain.

For COFDM, when a deep fade occurs in the channel the bits within the deep fade are erased. Interleaving the

bits across different frequency bins distributes the energy within a symbol among different sub-carriers. Since

distinct sub-carriers undergo different fading conditions, the probability that all the bits corresponding to a

symbol are lost, decreases significantly. An uncoded OFDM system cannot exploit frequency diversity.

Modal dispersion is the dominant performance limiting factor in MMFs. The advantage of MMFs over

single mode fibers (SMFs) is their relaxed coupling tolerance, their larger core diameters, which lead to reduced system-wide installation and maintenance costs. MMFs allow the propagation of multiple guided modes albeit

with different propagation constants. The difference in the mode propagation times leads to intermodal

dispersion, which severely limits the fiber bandwidth.

In ROF, COFDM impact has been demonstrated with MMF as a promising modulation scheme for

mitigating the modal dispersion penalty [6]. By implementing coding and interleaving across sub-carriers, the

Performance Analysis of Coded OFDM Signal for Radio over Fiber Transmission


effect of multipath fading is minimised since interleaving reorganises the number of bits in a way so as to avoid

the effects of fading. Convolutional coding is being used commonly in COFDM systems, and it can give coding

gain at different coding rates [7]. Thus, COFDM makes the transmitted signal robust to multipath fading and

distortion. Also it has been observed in [8] that the frequency selectivity becomes significant with increasing the

length of the MMF. This reveals the need for COFDM.

II. SIMULATION SETUP FOR RADIO OVER FIBER SYSTEM RoF system is very cost effective because localization of signal processing in central station and use of

a simple base station. It realizes transparent transformation between RF signal and optical signal. Fig 1 shows

implementation approach for transmitting convolutionally encoded data over a multi mode fiber.

Fig. 1.Block Diagram of COFDM based RoF System

Pseudo-random bit sequence generator (PRBSG) data format is used which is convolutionally encoded

at rate ½ and generator polynomials 1338 and 1718. For downlink simulation, a narrow bandwidth continuous wave (CW) (wavelength, 1300 nm) from laser diode is modulated via a Mach-Zehnder Modulator (MZM). An

ideal pre amplifier before optoelectronic conversion by PIN-PD having centre frequency of 1300 nm and post

amplifier is used at uplink simulation. Optical link i.e. a multi mode fiber of length 2 km is used. In receiver

section, optical signal is detected by a PIN-photodiode. Q-factor values of signals are measured by BER

analyzer at base station.

III. COFDM MODEL The implementation process of COFDM is as follows:

The data bits are convolutionally encoded with a code rate 1/2.

The encoded bits are bit-interleaved and then mapped into in-phase and quadrature components of the

complex symbol.

The complex symbols are modulated by OFDM.

At the receiver, direct detection is used. Table 1 summarises COFDM parameters.

The decoding process employs the hard-decision viterbi decoding algorithm.

Table 1.COFDM parameters

Parameter Value

Code rate ½

Generator polynomials 1338, 1718

Constraint length 7

IV. MMF RESPONSE MODEL The implementation process of MMF is as follows:

COFDM signal generated out of the OFDM transmitter is injected into the laser diode (LD).

The optical intensity signal out of the LD is fed into an MMF followed by a photodetector.

PRBSG Coder OFDM

Modulator

E/O Converter

O/E Converter OFDM

Demodulator

Decoder BER

Analyzer

PRBSG



The received COFDM signal is demodulated and decoded to retrieve the data. Table 2 summarizes MMF

parameters.

Table 2.MMF parameters

Parameter Value

Fiber dispersion -100 ps/(nm

km)

Fiber attenuation 1.25 dB/km

Wavelength

Fiber length

1300 nm

2 km

V. RESULTS AND DISCUSSION In this section we have tried to investigate the impact of modulation techniques on RoF system. For

RoF approach, multiple data signals are analyzed using optical and RF spectrum analyzers.

Fig. 2.Comparative analysis of QAM using optical amplifier and EDFA

From the Fig 2, above it can be seen that for bit rates used in simulation, QAM with optical amplifier

gives better Quality factor than QAM with EDFA. Also as the bit rate increases the quality factor begins to

degrade for both the modulation techniques for values greater than equal to 800 Mbps and falls below the value

required for communication purpose. The fall in the Q factor is due to increase in dispersion as bit rate

increases.



Fig. 3.Comparative analysis of QPSK using optical amplifier and EDFA

From the Fig 3, above it can be seen that for bit rates used in simulation, QPSK with optical amplifier

gives better Quality factor values than QPSK with EDFA. Also as the bit rate increases the quality factor begins

to degrade for both the modulation techniques for values greater than equal to 800 Mbps and falls below the

value required for communication purpose. The fall in the Q factor is due to increase inn dispersion as bit rate

increases.

From above two figures 2 and 3, it can be analyzed that either of the modulation techniques i.e. QAM

or QPSK gives better performance with optical amplifier than used with EDFA.

VI. CONCLUSION From the results for Radio over MMF with link length of 2 km (medium haul communication) based

upon the comparative analysis between QAM and QPSK modulation techniques with optical amplifier and

EDFA for same values of increasing bit rates, optical amplifier gives better results than EDFA for either of the

modulation techniques. After 800 Mbps the quality factor value falls below permissible value for

communication.

REFERENCES [1] Hamed Al Raweshidy, Shozo Komaki, Radio over fiber technologies for mobile communications networks, pp. 128-132 (2002).

[2] Guennec, Y.l., Pizzinat, A., Meyer, S., et al., Low-cost transparent radio-over-fiber system for in- building distribution of UWB signals, pp. 2649–2657 (2009).

[3] Stott, J.H., The DVB terrestrial (DVB-T) specification and its implementation in a practical modem, pp. 255-260 (1996).

[4] A.M. Matarneh, S.S.A. Obayya, Bit-error ratio performance for radio over multimode fiber system using coded orthogonal frequency division multiplexing, pp. 151–157 (2011).

[5] Arun Agarwal, Kabita Agarwal, Design and Simulation of COFDM for High Speed Wireless Communication and Performance

Analysis, pp. 22-28 (2011).

[6] Dixon, B.J., R.D., Iezekiel, S., Orthogonal frequency-division multiplexing in wireless communication systems with multimode fiber feeds, pp. 1404–1409 (2001).

[7] Prasad, R., OFDM for wireless communication systems, Artech House, (2004).

[8] Aser M. Matarneh, S. S. A. Obayya, I. D. Robertson, Coded orthogonal frequency division multiplexing transmission over graded

index multimode fiber, (2007).




Performance Analysis of Linear Block Code, Convolution code

and Concatenated code to Study Their Comparative

Effectiveness.

Mr.Vishal G. Jadhao, Prof.Prafulla D. Gawande 1, 2, (Department Of Electronics and Telecommunication, Sipna College of Engineering and Technology

Amravati, India) 2(Department of Electronics & Telecommunication Engineering, SIPNA College of Engineering and

Technology, Amravati – 444701 (M.S.)

ABSTRACT: In data communication the codes are used to for security and effectiveness which is thoroughly

followed by the network. Here in LBC (linear block code), COC (convolution code), Concatenated codes (CC) are used. The work presented here is used the to make comparative effectiveness of this codes in order to make

secure data analysis. Error coding is a method of detecting and correcting these errors to ensure information is

transferred intact from its source to destination. Error coding uses mathematical formulas to encode data bits at

the source into longer bit words for is transmission. Decoding of the code word is possible at side of receiver.

The extra bits in the code word provide redundant bit, according to the coding scheme used, will allow the

destination to use the decoding process to determine if the communication mediums expected error rate, signal

to noise ratio and whether or not data retransmission is possible. Above coding technique implemented system

can show error of exact bit in given data this error display bitwise. Faster processors and better

communications technology make more complex coding schemes, with better error detecting and correcting

capabilities, possible for smaller embedded systems, allowing for more robust communications.

Keywords: Concatenated code error control coding technique, Convolutional codes error control coding

technique, Embedded Communication, Fault Tolerant Computing, linear block codes error control coding

technique, Real-Time System, Software Reliability

I. INTRODUCTION Environmental interference and physical defects in the communication medium can cause random bit

errors during data transmission. Error coding is a method of detecting and correcting these errors to ensure

information is transferred intact from its source to its destination. Error coding is used for fault tolerant

computing in computer memory, magnetic and optical data storage media, satellite and deep space

communications, network communications, cellular telephone networks, and almost any other form of digital

data communication. Error coding uses mathematical formulas to encode data bits at the source into longer bit

words for transmission. The “code word” can then be decoded at the destination to retrieve the information. The

extra bits in the code word provide redundancy that, according to the coding scheme used, will allow the

destination to use the decoding process to determine if the communication mediums expected error rate, and

whether or not data retransmission is possible. Faster processors and better communications technology make more complex coding schemes, with better error detecting and correcting capabilities, possible for smaller

embedded systems, allowing for more robust communications. During digital data transmission error will

occurring this error can detect and correct this error binary signal and original signal can show bitwise. System

can implemented using MATLAB.The introduction of the paper should explain the nature of the problem,

previous work, purpose, and the contribution of the paper. The contents of each section may be provided to

understand easily about the paper.

II. CODING TECHNIQUES 1. Overview of Error Control Coding: Error-correcting codes are used to reliably transmit digital data over unreliable communication channels subject to channel noise. When a sender wants to transmit a possibly very

long data stream using a block code, the sender breaks the stream up into pieces of some fixed size. Each such

piece is called message and the procedure given by the block code encodes each message individually into a

codeword, also called a block in the context of block codes. The sender then transmits all blocks to the receiver,

who can in turn use some decoding mechanism to (hopefully) recover the original messages from the possibly

Performance Analysis of Linear Block Code, Convolution code and Concatenated code to Study Their

Comparative Effectiveness

www.iosrjournals.org 54 |Page

corrupted received blocks. The performance and success of the overall transmission depends on the parameters

of the channel and the block code.

The theoretical contribution of Shannon’s work in the area of channel coding was a useful definition of

“information “and several “channel coding theorems” which gave explicit upper bounds, called the channel

capacity, on the rate at which “information” could be transmitted reliably on a given. In the context of this

paper, the result of primary interests the “noisy channel coding theorem for continuous channels with average

power limitations.” This theorem states that the capacity C of a band limited additive white Gaussian noise

(AWGN) channel with bandwidth W, a channel model that approximately represents many practical digital

communication and storage systems.

1.1. Linear Block Codes: Linear block codes are so named because each code word in the set is a linear

combination of a set of generator code words. If the messages are k bits long, and the code words are n bits long

(where n > k), there are k linearly independent code words of length n that form a generator matrix. To encode

any message of k bits, you simply multiply the message vector u by the generator matrix to produce a code word

vector v that is n bits long. Linear block codes are very easy to implement in hardware, and since they are

algebraically determined, they can be decoded in constant time. They have very high code rates, usually above

0.95. They have low coding overhead, but they have limited error correction capabilities. They are very useful in

situations where the BER of the channel is relatively low, bandwidth availability is limited in the transmission,

and it is easy to retransmit data.

One class of linear block codes used for high-speed computer memory are SEC/DED (single-error-correcting/double-error-detecting) codes. In high speed memory, bandwidth is limited because the cost per bit is

relatively high compared to low-speed memory like disks . The error rates are usually low and tend to occur by

the byte so a SEC/DED coding scheme for each byte provides sufficient error protection. Error coding must be

fast in this situation because high throughput is desired. SEC/DED codes are extremely simple and do not cause

a high coding delay.

1.2. Convolutional Codes: Convolutional codes are generally more complicated than linear block codes, more

difficult to implement, and have lower code rates (usually below 0.90), but have powerful error correcting

capabilities. They are popular in satellite and deep space communications, where bandwidth is essentially

unlimited, but the BER is much higher and retransmissions are infeasible. Convolutional codes are more

difficult to decode because they are encoded using finite state machines that have branching paths for encoding

each bit in the data sequence. A well-known process for decoding convolutional codes quickly is the Viterbi Algorithm. The Viterbi algorithm is a maximum likelihood decoder, meaning that the output code word from

decoding a transmission is always the one with the highest probability of being the correct word transmitted

from the source.

The main difference between block codes and the Convolutional codes (or recurrent) codes is the

following: In block code, a block of n digits generated by the encoder in a particular time unit depends only on

block of k input message digits generated by the encoder in a time unit depends on not only the block of k

message digits within that time unit, but also on the preceding (N-1) blocks of message digits (N>1). Usually the

values of k and n will be small.

1.3. Concatenated codes: The field of channel coding is concerned with sending a stream of data at the highest

possible rate over a given communications channel, and then decoding the original data reliably at the receiver,

using encoding and decoding algorithms that are feasible to implement in a given technology.

Shannon's channel coding theorem shows that over many common channels there exist channel coding

schemes that are able to transmit data reliably at all rates R less than a certain threshold C, called the channel

capacity of the given channel. In fact, the probability of decoding error can be made to decrease exponentially as

the block length N of the coding scheme goes to infinity. However, the complexity of a naive optimum decoding

scheme that simply computes the likelihood of every possible transmitted codeword increases exponentially

with N, so such an optimum decoder rapidly becomes infeasible .

Dave Forney showed that concatenated codes could be used to achieve exponentially decreasing error

probabilities at all data rates less than capacity, with decoding complexity that increases only polynomially with

the code block length.

http://en.wikipedia.org/wiki/Channel_coding

http://en.wikipedia.org/wiki/Communications_channel

http://en.wikipedia.org/wiki/Noisy-channel_coding_theorem

http://en.wikipedia.org/wiki/Channel_capacity



http://en.wikipedia.org/wiki/Dave_Forney




III. INDENTATIONS AND EQUATIONS

1. Linear block code:

Theorem 1: Let a linear block code C have a parity check matrix H. The minimum distance of C is equal to the

smallest positive number of columns of H which are linearly dependent.

This concept should be distinguished from that of rank, which is the largest number of columns of H

which are linearly independent.

Proof:

Let the columns of H be designated as d0; d1; : : : ; dn1. Then since cHT = 0 for any codeword c, we have

0 = c0d0 + c1d1 +……+ cn1dn1

Let c be the codeword of smallest weight, w = w(c) = dmin. Then the columns of H corresponding to the

elements of c are linearly dependent. 2 Based on this, we can determine a bound on the distance of a code:

dmin = n- k + 1: The Singleton bound

This follows since H has n k linearly independent rows. (The row rank = the column rank.) So any combination

of n k + 1 columns of H must be linearly dependent.

For a received vector r, the syndrome is

s = rHT

Obviously, for a codeword the syndrome is equal to zero. We can determine if a received vector is a codeword.

Furthermore, the syndrome is independent of the transmitted codeword. If r = c + e,

s = (c + e)HT

= eHT

Furthermore, if two error vectors e and e’ have the same syndrome, then the error

Vectors must differ by a nonzero codeword. That is, if

eHT = e’HT then

(e – e’) HT = 0

This means they must be a codeword.

2. Convolutional Code:

2.1 Coding and decoding with Convolutional Codes: Convolutional codes are commonly specified by three

parameters; (n, k, m). n = number of output bits

k = number of input bits

m = number of memory registers

The quantity k/n called the code rate is a measure of the efficiency of the code. Commonly k and n

parameters range from 1 to 8, m from 2 to 10 and the code rate from 1/8 to 7/8 except for deep space

applications where code rates as low as 1/100 or even longer have been employed. Often the manufacturers of

Convolutional code chips specify the code by parameters (n, k, L), the quantity L is called the constraint length

of the code and is defined by

Constraint Length, L = k (m-1)




The constraint length L represents the number of bits in the encoder memory that affect the generation

of the n output bits. The constraint length L is also referred to by the capital letter K, which can be confusing

with the lower case k, which represents the number of input bits. Sometimes K is defined as equal to product the

of k and m. Often in commercial spec, the codes are specified by (r, K), where r = the code rate k/n and K is the

constraint length. The constraint length K however is equal to L – 1.

2.2 Code Parameters and the Structure of the Convolutional code:The Convolutional code structure is easy

to draw from its parameters. First draw m boxes representing the m memory register. Then draw n modulo-2

adders to represent the n output bits. Now connect the memory registers to the adders using the generator

polynomial.

This is a rate 1/3 code. Each input bit is coded into 3 output bits. The constraint length of the code is 2.

The 3 output bits are produced by the 3 modulo-2 adders by adding up certain bits in the memory registers. The

selection of which bits are to be added to produce the output bit is called the generator polynomial (g) for that

output bit. For example, the first output bit has a generator polynomial of (1, 1, 1). The output bit 2 has a

generator polynomial of (0, 1, 1) and the third output bit has a polynomial of (1, 0, 1). The output bits just the

sum of these bits.

v1 = mod2 (u1 + u0 + u-1)

v2 = mod2 ( u0 + u-1)

v3 = mod2 (u1 + u-1)

from fig.(1)

The polynomials give the code its unique error protection quality. One (3,1,4) code can have completely different properties from an another one depending on the polynomials chosen.

3. Concatenated Codes:Let Cin be a [n, k, d] code, that is, a block code of length n, dimension k,

minimum Hamming distance d, and rate r =n/k, over an alphabet A:

Let Cout is a [N, K, D] code over an alphabet B with |B| = |A|k symbols:

The inner code Cin takes one of |A|k = |B| possible inputs, encodes into an n-tuple over A, transmits, and

decodes into one of |B| possible outputs. We regard this as a (super) channel which can transmit one symbol

from the alphabet B. We use these channel N times to transmit each of the N symbols in a codeword of Cout. The concatenation of Cout (as outer code) with Cin (as inner code), denoted Cout Cin, is thus a code of

length Nn over the alphabet A:[1]

It maps each input message m = (m1, m2, ..., mK) to a codeword (Cin(m'1), Cin(m'2), ..., Cin(m'N)), where

(m'1, m'2, ..., m'N) = Cout(m1, m2, ..., mK).

The key insight in this approach is that if Cin is decoded using a maximum-likelihood approach (thus

showing an exponentially decreasing error probability with increasing length), and Cout is a code with length N =

2nr that can be decoded in polynomial time of N, then the concatenated code can be decoded in polynomial time

of its combined length n2nr = O(N⋅log(N)) and shows an exponentially decreasing error probability, even

if Cin has exponential decoding complexity.[1] This is discussed in more detail in section decoding concatenated

codes.

In a generalization of above concatenation, there are N possible inner codes Cin,i and the i-th symbol in

a codeword of Cout is transmitted across the inner channel using the i-th inner code. The Justesen codes are

examples of generalized concatenated codes, where the outer code is a Reed-Solomon code

3.1. Theorem: The distance of the concatenated code Cout∘Cin is at least dD, that is, it is a [nN, kK, D'] code with D' ≥ dD.

http://en.wikipedia.org/wiki/Block_code

http://en.wikipedia.org/wiki/Dimension_(vector_space)

http://en.wikipedia.org/wiki/Hamming_distance

http://en.wikipedia.org/wiki/Code_rate

http://en.wikipedia.org/wiki/Concatenated_error_correction_code#cite_note-Forney-0

http://en.wikipedia.org/wiki/Maximum_likelihood_decoding

http://en.wikipedia.org/wiki/O_notation

http://en.wikipedia.org/wiki/Concatenated_error_correction_code#cite_note-Forney-0

http://en.wikipedia.org/wiki/Concatenated_error_correction_code#Decoding_concatenated_codes



http://en.wikipedia.org/wiki/Justesen_code

http://en.wikipedia.org/wiki/Reed-Solomon_code




Proof: Consider two different messages m1 ≠ m2 ∈ BK. Let Δ denote the distance between two codewords. Then

Thus, there are at least D positions in which the sequence of N symbols of the codewords Cout(m

1)

and Cout(m2) differ. For these positions, denoted i, we have

Consequently, there are at least d⋅D positions in the sequence of n⋅N symbols taken from the

alphabet A in which the two codewords differ, and hence

2. If Cout and Cin are linear block codes, then CoutCin is also a linear block code.

This property can be easily shown based on the idea of defining a generator matrix for the concatenated code in

terms of the generator matrices of Cout and Cin.

Figures and Tables:

1. Convolutional code:

Fig.1 Sequential Statement

1.1. polynomials are selected for given table:

Constraint

Length

G1 G2

3

4

5

6

7

8

1 1 0

1 1 0 1

1 1 0 1 0

1 1 0 1 0 1

1 1 0 1 0 1

1 1 0 1 1 1

1 1 1

1 1 1 0

1 1 1 0 1

1 1 1 0 1 1

1 1 0 1 0 1

1 1 1 0 0 1 1

http://en.wikipedia.org/wiki/Linear_block_code

http://en.wikipedia.org/wiki/Generator_matrix




1.2. Convolutional code resulted binary message and bitwise graph

Fig.2 Binary message of convolutional code

9

10

1 1 0 1 1 1

1 1 0 1 1 1 0

0 1

1 1 1 0 0 1 1 0 1

1 1 1 0 0 1 1 0 0

1




Fig.3 Bitwise graph of convolutional code

2. Linear block code resulted binary message and bitwise graph

Fig.4 Binary message of linear block code

Fig.5 Bitwise graph of linear block code




3. Concatenated code resulted bitwise graph

Fig.6 Concatenated code resulted graph bitwise.

4. BER to Eb/No graph

Fig.7 BER to Eb/No

IV. CONCLUSION 1 Show in the BER to Eo/No graph the convolutional code is very good capability of error correction then

linear block code.

2 Convolutional code is very difficult to implementation then linear block code.

3 Concatenated code is both binary and non binary data can be send through encoder most useful for error

correction capability.

4 Convolutional code is better than linear block code and concatenated code.




5 Important reasons to use coding are achieving dependable data storage in the face of minor data

corruption/loss mid gaining the ability to provide high precision I/O even on noisy transmission lines

such as cellular phones (error coding decouples message precision from analog noise level).

6 Most use full in real time system can show error bitwise exact bit is show in result.

7 Error signal can easily display on screen command window of matlab.

8 The error coding technique for an application should be picked based on The types of errors expected on

the channel (e.g., burst errors or random bit error)

9 Whether or not it is possible to retransmit the information (codes for error detection only or error

"detection and correction) 10 Expected error tale on the communication channel (high or low)

11 There are several tradeoffs between channel efficiency and the amount of coding/decoding logic

implemented

FUTURE APPLICATION AND DEVLOPMENT

1 Fault tolerant computing in computer memory

2 Magnetic and optical data storage media

3 Satellite and deep space communications

4 Network communications 5 Cellular telephone networks

6 Almost any other form of digital data communication

ACKNOWLEDGEMENTS We are thankful Prof. A. P. Thakare HOD (Department Of Electronics and Telecommunication),

Dr. Prof. A. A. Gurjar Sipna C.O.E.T. Amravati for their encouragement and support.

References 1. Kai Yang and Xiaodong Wang, Analysis of Message-Passing Decoding Of Finite-Length Concatenated Codes,IEEE Transaction On

Communication,VOL.59,No.8,August 2011.

2. Application of error control coding Deniel J. Costello, Jr. Fellow, IEEE, Joachim Hagenauer, Fellow, IEEE, Hideki Imai, Fellow,

IEEE, and Stephen B. Wicker, Sr. member, IEEE. IEEE Transactions on Information theory, Vol. 44, No. 6, October 98.

3. Error Correction in a Convolutionally Coded System,Wang,Member,IEEE,WaniunZhao,Member,IEEE,and Georgios B.

Giannakis,Fellow,IEEE TransactionCommunications,Vol.56,No.11,November 2008.

4. S. Lin and D. J. Costello, Error Control Coding. Englewood Cliffs, NJ: Prentice Hall, 1982.

5. W. W. Peterson and E. J. Weldon, Jr., Error Correcting Codes, 2nd ed. Cambridge, MA: The MIT Press, 1972.

6. V. Pless, Introduction to the Theory of Error-Correcting Codes, 3rd ed. New York: John Wiley & Sons, 1998.

7. K. Sam Shanmugam, Digital and Analog Communication Systems.

8. S. Lin and D. J. Costello, Error Control Coding. Englewood Cliffs, NJ: Prentice Hall, 1982.

9. M. Michelson and A. H. Levesque, Error Control Techniques for Digital Communication. New York: John Wiley & Sons, 1985.

10. W. W. Peterson and E. J. Weldon, Jr., Error Correcting Codes, 2nd ed. Cambridge, MA: The MIT Press, 1972.

11. V. Pless, Introduction to the Theory of Error-Correcting Codes, 3rd ed. New York: John Wiley & Sons, 1998.

12. C. Schlegel and L. Perez, Trellis Coding. Piscataway, NJ: IEEE Press, 1997

13. S. B. Wicker, Error Control Systems for Digital Communication and Storage. Englewood Cliffs, NJ: Prentice Hall, 1995.

First Author-Mr.Vishal G. Jadhao

He is also pursuing his M.E. in Digital Electronics from the sipna’s College of

Engineering and Technology, Amravati (India). His areas of interest are Digital

Communication System and Digital Signal Processing.

Second Author-Prof.Prafulla D. Gawande

He is currently working as Professor in Electronics and Telecommunication

Engineering Department, Sipna’s College of Engineering, Amravati (India)




Utilization of renewable energy resources

Vishnu vardhan.ch1

1(ECE department, AL-Aman College of engineering/ Jawaharlal Nehru technological university Kakinada,

India )

ABSTRACT : Man has gloriously innovated, discovered, profoundly invented some of the state of the art

technologies and designed modern structures that stand apart from other civilizations .yet there has been

growing concern of deteriorating mother earth. Of late, this very dooms day can just be limited to a sci-fi story unlike we approach extra terrestrial life or explore celestial bodies for mineral extraction. Until and unless the

very typical and running alternatives are implemented for this desperate eco-friendly transformations.

Human civilization has leaded the path to qualitative technology with greater efficiency than

previously used. Yet there has been a demerit for all the technologies that were developed ever since the idea of

having technology came. By this we put before you some of the exquisite ways to make use of pristine forces of

nature which have been trivialized since the era of modern age.

Keywords: EO, EPGSP, ME, SIE, ST .

I. INTRODUCTION This paper explains about two working models called simplified traveler and enhanced power

generation from solar panel. There are working models partially resembling with the simplified traveler but the ST is far more advanced. The ST is capable of converting solar energy and mechanical energy into electrical

energy through three different ways. this simplified traveler travels with the help of three power giving sources

which are renewable and non-polluting so that if one fails the other can be used , on the other hand using three

power giving sources does not mean of overexploitation , this could be best understood in the future pages of the

paper . Enhanced power generation from solar panels, when a solar panel is placed under the sun it converts

solar energy into electrical. lets say for example 10 volts can be produced with single solar panel , but through

the enhanced power generation we can produce produce 100 volts of electrical energy with the help of 10

concave reflecting surfaces on to the single solar panel . Due to the reflecting surfaces huge amount of light falls

on the solar panel and as a result it produces 10 times of its previous out put . [1]

II. ENERGY CONVERSION EFFICIENCY This is a simple definition of efficiency of a device that converts one energy form into another. : -

It is a number between 0 and 1, or between 0 and 100%. The key word in the definition is useful energy

output (EO). Were it not there, the efficiency of any device would be 100%, because of the law of conservation

of energy (which states that energy cannot be created or destroyed). Instead, we see that quite a few devices

used in daily life have very low efficiencies. The purpose of a device determines its useful energy output. For

example, we want light from a lamp, but we get mostly heat; only 5% of the energy input (electricity) is

converted into light, so the efficiency of a conventional incandescent light bulb is 5%.

It is obvious why fluorescent lights are preferable for heavy-duty use (e.g., basements, kitchens, public

buildings). Similarly, diesel engines (the kinds used in most trucks and in some cars, especially the European

ones) are typically more efficient than conventional spark-ignition engines (SIE) [2] The familiar term 'fuel economy' or ' mileage' (distance traveled per liter of fuel consumed) is

equivalent to the efficiency of an automobile; it is just expressed in different units. Instead of being expressed in

units of mechanical energy (work), the useful energy output is given as the average number of kilometers

traveled. And instead of being expressed in units of chemical energy, the energy input is given as the number of

liters of fuel [3]. Refer figure-(1)

III. MAXIMUM POWER TRANSWER THEOREM Maximum power transfer theorem states that, to obtain maximum external power from a source with a

finite internal resistance, the resistance of the load must be equal to the resistance of the source as viewed from the output terminals. The theorem results in maximum power transfer, and not maximum efficiency. If the

resistance of the load is made larger than the resistance of the source, then efficiency is higher, since a higher

percentage of the source power is transferred to the load, but the magnitude of the load power is lower since the

total circuit resistance goes up.[4]

http://www.ems.psu.edu/~radovic/Chapter%2018



If the load resistance is smaller than the source resistance, then most of the power ends up being

dissipated in the source, and although the total power dissipated is higher, due to a lower total resistance, it turns

out that the amount dissipated in the load is reduced. The theorem states how to choose (so as to maximize

power transfer) the load resistance, once the source resistance is given, not the opposite. It does not say how to

choose the source resistance, once the load resistance is given. Given a certain load resistance, the source

resistance that maximizes power transfer is always zero, regardless of the value of the load resistance.[5] The theorem was originally misunderstood (notably by Joule) to imply that a system consisting of an

electric motor driven by a battery could not be more than 50% efficient since, when the impedances were

matched, the power lost as heat in the battery would always be equal to the power delivered to the motor. In

1880 this assumption was shown to be false by either Edison or his colleague Francis Robbins Upton, who

realized that maximum efficiency was not the same as maximum power transfer. To achieve maximum

efficiency, the resistance of the source (whether a battery or a dynamo) could be made close to zero. Using this

new understanding, they obtained an efficiency of about 90%, and proved that the electric motor was a practical

alternative to the heat engine[6] .The condition of maximum power transfer does not result in

maximum efficiency(ME)

Consider three particular cases:

The efficiency is only 50% when maximum power transfer is achieved, but approaches 100% as the load resistance approaches infinity, though the total power level tends towards zero. Efficiency also approaches

100% if the source resistance can be made close to zero. When the load resistance is zero, all the power is

consumed inside the source (the power dissipated in a short circuit is zero) so the efficiency is zero[7]

IV. LAW OF CONSERVATION OF ENERGY The law of conservation of energy states that the amount of energy in a system will stay constant

throught time . Therefore this law is stating that no matter how much time goes by , the amount of energy in a

certain state of matter will remain the same , hence conserved , no matter how much time has passed . Therefore

energy cannot be created nor destroyed but it can be transformed from one form to another form .[8]

A motor is fixed to one of the rod and to the other end of the rod a dynamo is fixed. The axle of the

motor is perpendicular to the rod and the same with the dynamo. Now the tires are fixed to the axel of the motor

and also to the dynamo. The positive terminal of the motor is fixed to the positive terminal of the dynamo and

vice-versa. Let us for a while consider that efficiency of the dynamo and motor in conversion of the given input

is only 75% that means 25% of the given input is wasted, when input is converted into another form of

energy.[9]

Consider an electrical motor and a dynamo of coinciding ratings and parameters, connected to either

ends of a supporting rod. However care must be taken that these devices are port vise synchronized (with parallel terminals) .let each of these two devices attached with one rotating tire so that it would constitute to a

bicycle like movement. Now the arrangement is kept on the ground and is given an initial manual trust which

causes the whole arrangement to move forward, because of this moment the dynamo will rotate. It produces 75

% of electrical energy to which 100% of input mechanical energy is given (cause of pushing). The 75% of the

electrical output produced by the dynamo is given as input to the motor; it converts its input into 50% of

mechanical energy. As a result the motor rotates the wheel and the whole arrangement moves forward. Again

the dynamo rotates and converts its 50% of the mechanical input into 25% of electrical out, which is in turn

given to the motor, whose mechanical conversion will be 0%. This results in deceleration of the system and

finally the system stops. This example proves that conversion of energy involves in wastage of energy and

100% of energy conversion is not possible.[10] . refer fig-(2) and table-1

V. SIMPLIFIED TRAVELLER There are many vehicles capable of converting solar power into mechanical one and the electrical

power to mechanical. The vehicle most popular for electrical power conversion into mechanical energy is E-

bike. Due to the limited battery charge, it cannot travel long distances as there are no alternatives except finding

a power source (like E-bunks ) , therefore the only solution is to be precautious . Now there arises the necessity

of having a vehicle which is capable of converting both solar energy and mechanical energy into electrical

energy and it should not need any external source to be recharged with in the mid way. This paper puts forward the model called simplified traveler (ST) which possess all the above features.

The simplified traveler is basically a bicycle, with advanced features. The simplified traveler is

powered by solar panel, dynamo and simple muscular pedaling so this has three power sources , when one kind

of energy source fails the other can be utilized . In this scenario the ST has two alternate power producing

sources which under any circumstances can set the ST in motion. For an instance if it‟s cloudy the solar panel

cannot produce sufficient energy to set ST in motion. In this case when ST is pedaled the dynamo also rotates

http://en.wikipedia.org/wiki/James_Prescott_Joule

http://en.wikipedia.org/wiki/Thomas_Edison

http://en.wikipedia.org/wiki/Francis_Robbins_Upton

http://en.wikipedia.org/wiki/Dynamo

http://en.wikipedia.org/wiki/Electric_motor

http://en.wikipedia.org/wiki/Heat_engine

http://en.wikipedia.org/wiki/Electrical_efficiency

http://en.wikipedia.org/wiki/Short_circuit



which produces energy and is stored in the battery, when peddling is stopped the stored energy in the power

storage can be utilized to set the ST in motion.

There has a chain from the pedal and also from the dynamo which is given to the rare twin chain crank

wheel which could accommodated two chains .peddling the ST corresponds to the rotation of the dynamo and

wheel .this results in forward moment of ST and production of electrical energy by the dynamo, this generated

electrical energy is stored in the power storage device. If in case the person traveling on ST is unable to pedal then the solar panel can be used.

The simplified traveler is very simple in its working and mechanism. The efficiency of the simplified

traveler depends on the kind of dynamo used in producing the electricity when rotated [11].There are different

ways to increase the efficiency of the dynamo is

1. by increasing the number windings

2. by increasing the pole strength , magnetic flux density of the magnet

3. by increasing the thickness of the windings refer fig-(3)

VI. ENHANCED POWER GENERATION FROM A SOLAR PANNEL This is a simple arrangement of mirrors cut in small uniform dimensions like square. It includes the

positioning of such mirrors on to a concave surface which is in turn tilted towards the sun. The arrangement is

such that the reflected light from it gets focused on to a particular area. We should arrange a solar panel of high

power or any heat conversion energy device.

This arrangement facilitates to produce the same amount of energy from N number of individual solar

panels when compared with a single solar panel assisted by concave reflecting surfaces [9]. As a result the cost

of production reduces thousands of times. Because of this arrangement thousands of joules of energy can be

concentrated at one point from where thousands of watts of energy can be produced with less loss in energy

conversion [12]. Refer fig-(4) (5). Thus EPGSP is very useful in power generation.

VII. FIGURES AND TABLES

Fig-(1) Efficiency of conversion device.

tyres

dynamomotor

rod

positive terminal

Negative terminal

BEST EXAMPLE FOR LAW OF CONSERVATION OF ENERGY :

CONNECTING DYNAMO AND MOTOR

tyres

dynamomotor

rod

positive terminal

Negative terminal

BEST EXAMPLE FOR LAW OF CONSERVATION OF ENERGY :

CONNECTING DYNAMO AND MOTOR

Fig (2): Example for law of conservation of energy.



MO DE L O F S IMP L IF IE D T R AVE L L E R

Solar panel

dynamo

chain

Lower twin

chain Crank

wheel

Power

Storage device

Front

tire

pedal

Upper crank wheel

seat

side crank wheel

Figure (3): Simplified traveler

Fig(4): Circular array of reflecting surfaces around solar tower.

BATTERY

SOLAR

PANNEL

SUN

INCIDENT RAYS FROM SUN

REFLECTED RAYS FROM CONCAVE

REFLECTOR

CONCAVE

REFLECTORS

EVENLY

CUT

MIRRORED

SURFACE

Figure (5): Enhanced power generation from solar panels



TABLE 1: Energy transformation table

VIII. SIMULATION RESULTS

Fig (6): output voltage of a solar panel

Fig(7): out put voltage of a motor

Fig(8): out put voltage of a dynamo



IX. CONCLUSION The paper finally explains the utilization of renewable energy resources, in the way that they do not

pollute the environment. More over the simplified traveler can be used and constructed with less effort, the only

limitation of the ST is more number of people cant travel on it at once. The enhanced power generation from solar panel helps in producing power at higher rate with the help of a single panel. This reduces the usage of

more number of solar panels to produce the same amount of energy which is obtained with the help of the

enhanced power generation from the solar panel

REFERENCES [1] Georg Hille, Werner Roth, and Heribert Schmidt, „„Photovoltaic systems,‟‟ Fraunhofer Institute for Solar Energy Systems, Freiburg,

Germany, 1995.

[2] OKA Heinrich Wilk, „„Utility connected photovoltaic systems,‟‟ contribution to design handbook, Expert meeting Montreux, October

19–21, 1992, International Energy Agency (IEA): Solar Heating and Cooling Program.

[3] Stuart R. Wenham, Martin A. Green, and Muriel E. Watt, „„ Applied photovoltaics,‟‟ Centre for photovoltaic devices and systems,

UNSW.

[4] N. Ashari, W. W. L. Keerthipala, and C. V. Nayar, „„A single phase parallel connected Uninterruptible power supply/Demand side

management system,‟‟ PE-275-EC (08-99), IEEE Transactions on Energy Conversion, August 1999.

[5] C. V. Nayar, and M. Ashari, „„Phase power balancing of a diesel generator using a bidirectional PWM inverter,‟‟ IEEE Power

Engineering Review 19 (1999).

[6] C. V. Nayar, J. Perahia, S. J. Philips, S. Sadler, and U. Duetchler, „„Optimized powerelectronic device for a solar powered centrifugal

pump,‟Journal of the Solar Energy Society of India, SESI Journal 3(2), 87–98 (1993).

[7] Ziyad M. Salameh, and Fouad Dagher, „„The effect of electrical array reconfiguration on the performance of a PV-powered

volumetric water pump,‟‟ IEEE Transactions on Energy Conversion 5 653–658 (1990).

[8] C. V. Nayar, S. J. Phillips, and W. L. James, T. L. Pryor, D. Remmer, „„Novel wind/diesel/battery hybrid system,‟‟ Solar Energy 51,

65–78 (1993).

[9] W. Bower, „„Merging photovoltaic hardware development with hybrid applications in the U.S.A.‟‟ Proceedings Solar ‟93 ANZSES,

Fremantle, Western Australia (1993).

[10] Carlson, D. E. 1995. “Recent Advances in Photovoltaics,” 1995 Proceedings of the Intersociety Engineering Conference on Energy

Conversion 1995, p. 621-626.

[11] www.google.com

[12] www.en-wikipedia.org

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