Heterogeneous Platform for IEDs Connection on a Fault

UNIVERSIDAD DE LOS ANDES

Heterogeneous Platform for IEDs

Connection on a Fault Location Engine of

Distribution Systems with DG using

DSSim-PC

David Felipe Celeita Rodrıguez

Submitted in fulfilment of the requirements for the Degree of

Master of Science in Electrical Engineering

Engineering Faculty

Department of Electrical and Electronic Engineering

June 25 - 2014

http://www.uniandes.edu.co/

mailto:[email protected]

https://ingenieria.uniandes.edu.co/

https://electrica.uniandes.edu.co/

Author’s Declaration

1. I am aware that any fraud in this thesis is considered a serious offense in college. By

signing, deliver and present this proposal Thesis or Graduation Project, I express testi-

mony that this proposal was developed in accordance with standards established by the

University. Similarly, assure you that I did not participate in any kind of fraud and at

work concepts or ideas that are taken from other sources are properly expressed.

2. I am aware that the work that I perform include ideas and concepts of the author and

the Advisor and may include course materials or previous work in the University and

therefore, give proper credit and I will use this material in accordance with human rights

standards copyright. Likewise, I will not publications, reports, articles and presentations

at conferences, seminars or conferences without review or authorization of the Counsel

who represent in this case the University.

Signature:

Nombre: David Felipe Celeita Rodrıguez

Codigo: 200614458

C.C.: 1’014.193.495 de Bogota

Date: June 25 -2014

i


Abstract

Engineering Faculty


One of the most important modules on the distribution management system (DMS) is the

fault location engine, which requires an effective algorithm to identify the fault line channel

from the data provided by different intelligent electronic devices (IEDs). The accuracy of this

process leads to smart features of intelligent network monitoring and efficient isolation. This

paper proposes the algorithm core of a fault locator software, based on undirected Dijkstra im-

plemented in MATLAB, and co-simulated in three standard feeders systems with distributed

generation using DSSim-PC and integrated in LabVIEW as a whole framework of a fault loca-

tion manager. Furthermore, a first prototype of a test bench is proposed and implemented as

an academic platform for research objectives and Advanced Distributed Automation (ADA)

purposes.

Keywords: ADA, Smart Grid, Fault Location Framework, DSSim-PC, IEDs, Distributed

Generation, RT-HIL Applications.





Abstract

Engineering Faculty


Uno de los modulos mas importantes en el sistema de gestion de distribucion (DMS) es el

motor de localizacion de fallas, el cual requiere un algoritmo efectivo para identificar el canal

de falla a partir de los datos proporcionados por los diferentes dispositivos electronicos in-

teligentes (IED). La exactitud de este proceso conduce a funciones inteligentes de supervision

de la red electrica y el aislamiento eficiente de fallas, para tomar posteriormente diferentes

decisiones cruciales como puede ser una reconfiguracion del sistema. En este trabajo se pro-

pone el nucleo del algoritmo para un software localizador de fallas basado en la metodologıa

de Dijkstra no direccionado implementado en MATLAB, incluyendo la co-simulacion en tres

sistemas alimentadores estandar con la generacion distribuida mediante DSSim-PC e inte-

grados en LabVIEW en su conjunto, como un motor de gestion de localizacion de fallas.

Ademas, se propone un primer prototipo de un banco de pruebas implementado como una

plataforma academica para objetivos de investigacion y con fines de realizacion de propuestas

en Automatizacion Avanzada (ADA).

Palabras Clave: ADA, Smart Grid, Fault Location Framework, DSSim-PC, IEDs, Dis-

tributed Generation, RT-HIL Applications.




Acknowledgements

Foremost, I would like to express my sincere gratitude to my advisor Prof. Gustavo Ramos

Ph.D. for the continuous support of my Master study and research, for his patience, motiva-

tion, enthusiasm, and immense knowledge. His guidance helped me in all the time of research

and writing of this thesis.

Besides my advisor, I would like to thank the rest of my thesis committee: MSc. Davis Mon-

tenegro, Prof. Ricardo Moreno Ph.D., and Prof. Fredy Segura Ph.D, for their encouragement,

insightful comments, and hard questions.

I thank my fellow labmates in Power and Energy Research Team, undegraduate students and

Ph.D students, in particular, i am grateful to MSc. Miguel Hernandez for enlightening me

the first glance of research.

Last but not the least, I would like to thank my family: my parents Olga Rodrıguez and

William Celeita, my twin brother Sebastian Celeita for supporting me spiritually throughout

my life. I also would like to thank to my wonderful inspiration Natalia Lozada for her patience

and love. To my friends and classmates.

This work was supported by COLCIENCIAS under the program “Jovenes Investigadores e

Innovadores 2012”.

iv

Contents

Author’s Declaration i

Abstract ii

Acknowledgements iv

List of Figures vii

List of Tables viii

Abreviations ix

1 Introduction 1

1.1 Fault Location Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Problem Context 4

2.0.1 General Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.0.2 Scpecific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.0.3 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.0.4 Description of the solution . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3 Fault Location Algorithm 10

3.1 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.2 Current Selection Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.3 Inverted Weights Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.1.4 Dijkstra’s Algorithm and Fault Line Channel . . . . . . . . . . . . . . . 13

3.1.5 Fault Decision Vector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Labview Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.1 Algorithm’s Process Order . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.2 Fault Location Routine - Labview . . . . . . . . . . . . . . . . . . . . . 16

v

Contents vi

3.2.3 Example: Fault Location Channel IEEE 13 Nodes . . . . . . . . . . . . 17

4 Platform Integration in LabVIEW 20

5 Fault Location Algorithm Validation 23

5.1 Test Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.1.1 IEEE 13 Node Test Feeder . . . . . . . . . . . . . . . . . . . . . . . . . 24



6 Heterogeneous Platform: Design and Construction 31

6.1 Hardware Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

6.2 Software Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.3 Fault Scenario: A Basic Example . . . . . . . . . . . . . . . . . . . . . . . . . . 35

6.3.1 RT-HIL Data Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . 36

7 Results Discussion and Conclusions 39

7.1 Results Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

7.1.1 Fault location on IEEE 13 nodes . . . . . . . . . . . . . . . . . . . . . . 39

7.1.2 Fault location on IEEE 34 nodes with DG . . . . . . . . . . . . . . . . . 40

7.1.3 Fault location on IEEE 123 nodes with DG . . . . . . . . . . . . . . . . 41

7.1.4 Heterogeneous Platform performance . . . . . . . . . . . . . . . . . . . . 43

7.1.5 Basic case: Oscilograph results . . . . . . . . . . . . . . . . . . . . . . . 45

7.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

List of Figures

2.1 Automation Level Context [19]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Outage Management Flowchart [19]. . . . . . . . . . . . . . . . . . . . . . . . . 6

2.3 Codensa Control Center, Bogota, Colombia. . . . . . . . . . . . . . . . . . . . . 7

2.4 Thesis Problem Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1 Algorithm Blocks [24]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.9 Flux Diagram - Fault Location Algorithm . . . . . . . . . . . . . . . . . . . . . 19

4.1 Software Integration Model [24] . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.2 User Interface - LabVIEW [24] . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.1 IEEE Test case 13 nodes on DSSim-PC [24] . . . . . . . . . . . . . . . . . . . . 24

5.2 IEEE Test case 34 nodes on DSSim-PC [24] . . . . . . . . . . . . . . . . . . . . 25

5.3 IEEE Test case 123 nodes on DSSim-PC [24] . . . . . . . . . . . . . . . . . . . 25

6.1 Prototype of the Hardware-In-The-Loop Test Bench proposed. [24] . . . . . . . 32

6.2 Constructed Test Bench . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6.3 Software Structure: Producer - Consumer Scructure . . . . . . . . . . . . . . . 34

6.5 RT-HIL Data Consistency: Amplification . . . . . . . . . . . . . . . . . . . . . 36

6.6 RT-HIL Data Consistency:TCC and Trip times . . . . . . . . . . . . . . . . . . 37

7.1 IEEE Test case 13 nodes Fault Programming and Location Result. [24] . . . . 40



7.4 Current Line A - Harmonics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7.5 Voltage Line A - Harmonics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7.6 NormalOperation IED Oscilography . . . . . . . . . . . . . . . . . . . . . . . . 43

7.7 Over Current Fault - IED Oscilography . . . . . . . . . . . . . . . . . . . . . . 43

7.8 Trip for Reclose Trial - IED Oscilography . . . . . . . . . . . . . . . . . . . . . 43

7.9 Successful Reclosing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

7.10 Protection Function Tested and Future Work . . . . . . . . . . . . . . . . . . . 45

vii

List of Tables

5.1 Simulated Scenarios for IEEE Test Case 13 Nodes . . . . . . . . . . . . . . . . 26



6.1 Basic Electric Case Information . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7.1 Test Feeder Results [24] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

viii

Abreviations

DER Distributed Energy Resource

DG Distributed Generator

DNO Distributed Network Operator

ILS Intelligent Electronic Device

MG Micro-Grid

DMS Distribution Management System

SG Smart Grid

FMS Fault Management System

FLC Fault LLocation Channel

RT Real Time

HIL Hardware In the Loop

ix

Chapter 1

Introduction

The increasing research work on power grid nowadays, have impulsed important challenges to

solve different applications on distribution systems complexity [1],[2],[3],[4]. Such applications

could be summarized as follows:

• Grid, product and equipment monitoring.

• Capacitor banks control.

• Fault location, isolation and restoration.

• Metering and control.

• Grid reconfiguration.

• Adaptive protections.

• Volt & Var Control.

• Event recording.

• Communications monitoring.

The context offers multiple working fields applying new technologies and modern equipment

to lead to a better grid, more reliable and efficient. ADA will enable the distribution feeders

1

Chapter 1. Introduction 2

to be selfhealing making new energy resources capable to be easily attached to the objective

network known as Smart Grid. Many of this solutions have been already implemented in real

grids around the world, like Canada [1], UK [5] and even Ghana [6] with results beyond the

expectations.

Distribution Management Systems (DMS) and IEDs interaction with a Fault Location En-

gine is the main focus of this study. Once a fault location background work is defined and

ADA application context is established, the paper will introduce a fault location algorithm

for distribution systems with DG. Then, a hybrid co-simulation platform is proposed, de-

signed and implemented to work with feeders hardware such as IEDs in order to perform

different scenarios on a real time hardware-in-the-loop architecture and test ADA solutions

and methodologies.

1.1 Fault Location Background

Fault location has allowed a breakthrough in the proper, effective and fast fault restoration of

distribution systems since its early development. The inclusion of intelligent electronic devices

(IEDs), which are capable of storing and recording a considerable amount of information, made

an improvement in location accuracy, robustness and speed of algorithms [7]. It highly impacts

the feeder reliability and power quality supplied to the customers. Given that at present, a

second context that should be included in a smart fault location (SFL) is the installation

of distributed generation (DG) as a model of renewable energy development. Therefore new

methodologies for fault location systems must consider the changes in the network to find

an optimal solution [8]. This paper focuses on radial distribution systems with DG, where

the current magnitudes and directions bring a level of complexity to design a fault location

algorithm.

Based on the classification proposed in [9], recent studies of fault location algorithms may be

categorized as follows:

• Intelligent processing of trouble calls [10].

Chapter 1. Introduction 3

• Fault location based on fault distance calculation [11].

• Fault location using transient signal features [12].

• Model-based fault location [13, 14].

It is well known that fault location solutions for transmission systems are not totally com-

patible to be implemented on distribution systems, and that is the reason why fault location

studies need to consider certain limitations [7]. For example, the data type constrains the set

of fault location algorithms such as the apparent impedance measurement, direct three-phase

circuit analysis, traveling waves, superimposed components, power quality monitoring data

and artificial intelligence.

The proposed method in this paper could be identified as based-model fault location algo-

rithm, improving its accuracy with the advantages of distributed systems radial topologies and

enhancing the data process from the information provided by RTUs or IEDs installed in the

main nodes. Specifically the algorithm input data will be current magnitude measurements,

as discussed in Section II. Description of the program and the integration of the simulation

platform is given in Section III. Section IV presents the fault location algorithm validation

with three different systems modified from IEEE 13, 34 and 123 nodes.

Section V will explain the hybrid platform proposed in this work. A Design and construction

description is shown with its hardware requirements and the used programming structure.

This chapter ends with a basic example test case.Section VI compiles the last two sections

results, adding a discussion of the algorithm results proposed on this paper, also the basic

test case performance on the hybrid platform is shown in this section. Conclusions are given

in Section VII.

Chapter 2

Problem Context

The energy industry is facing a transition in the formation of an electrical network infras-

tructure in order to expand the objectives towards a smart grid, safe, reliable and efficient

[15]. This transition involves acquiring devices smart metering (IEDs) which generate a large

amount of information is stored in substations, however in many cases the data is not sent to

the control center, so you can monitor and make decisions based on real information on the

network, giving appropriate use database produced by these teams. This is one of the main

challenges to overcome disadvantages of the conventional electricity grid [3], which from the

point of view of simulation models must integrate heterogeneous (communications, control

center and the grid) within the same room or platform to validate different scenarios. The

combination of various technological tools that allow remote monitoring of the grid, coor-

dination and operation of components thereon from different locations in real time, is the

overall goal of the applications of ADA (Advanced Distribution Automation for its acronym

in English) . Some studies in Canada [16] United States [17] and the UK [18] have compacted

the following set of applications where integration of the models is required:

• Network Monitoring (to improve reliability).

• Monitoring equipment and components of the distributed network (reduced maintenance

costs).

4

Chapter 2. Problem Context 5

• Monitoring of the product, free of capacitor banks and voltage control (improvement of

power quality - PQ).

To reduce the duration of outages, have made some progress on two strategies:

• Voltage and VAR Control.

• Troubleshooting.

The proposal raises directly address the application of Troubleshooting, although studies have

developed algorithms that optimize the location and isolation of a problem on the network,

yet to develop a comprehensive model that includes the communications platform, which for

this case is essential in the acquisition and availability of information, so that the control

center can make the right decision before an event, the model must be consistent with the

standards developed for the power sector, specifically the IEC 61850 standard.

Figure 2.1: Automation Level Context [19].

The approach is fully identified in the fourth layer of the automation pyramid introduced

by James Northcote [19] as shown in Fig.2.1. This layer refers to the direct automation of

distribution feeders using IEDs and using the communication infrastructure.

Under the context above in Fig.2.2, the fault management system is identified as one of the

objectives of the advanced automation ADA. Task is no longer an utopia of engineering to

become a research journal in hand with current industry practice. From many automation

ideas for fault location, the proposal focuses 4 are at the core of its construction:


Figure 2.2: Outage Management Flowchart [19].

• A fault management system as an operational control center module [Northcote]

• Use of new technologies for monitoring networks as IEDs [Kezunovic et al.]

• Inclusion of renewable energy in the models, which makes a distribution network in a

”multi-region power” system. [Ning]

• And finally a resource that allows progress both in research and planning for the new

smart grid. A platform that works with models constructed in DSSim-PC software while

interacting with real devices.

Within the entire activity diagram is proposed for system fault management, the proposed

force developed the following modules: Knowledge of electrical connection model system mea-

surement reports on the nodes (location of IEDs), estimate one FLC allocation and subsequent

clearance crews fault.

The Local context suggests that, currently the center of most modern control CODENSA

shown in Fig.2.3, has an engineering group for the remote information management and

communications, another for events scheduled (maintenance), another group for unscheduled

incidents (faults and crisis zone). The Corps of Engineers is further supported by with 50

crews per area for such incidents. Troubleshooting the low estimate offline measures with


Figure 2.3: Codensa Control Center, Bogota, Colombia.

software that takes a while to recognize the place failed. Most often prefer to skip this tool

and be guided by the calling user.

2.0.1 General Purpose

Given the above scenario is that the proposal should be defined to cover a module fault

management system which includes the use of IEDs for the measurements its connectivity

with the electric model to be integrated into the process of identifying the blocked region.

And adding the project to build the HIL platform that would validate such connectivity was

raised. Well localized failure generates optimal decisions for their solution, this improves the

performance of a Management System DMS Distribution Network.

The following idea describes the problem of Fig.2.4: Design and validate the model of inte-

grated heterogeneous platform to a fault management system by Real Time simulation based

on HIL arquitecture.

2.0.2 Scpecific Objectives

• Study the proposed models for the integration of connection systems to the electric grid.

• Identify the type of information necessary and sufficient for integration model of com-

munication to a Fault Management System (FMS).


Fault LocationManagement

FaultDetectionwith IEDs

IEDsConectivityassessment

Co-simulationintegration

RT-HIL Integration

Platform

Figure 2.4: Thesis Problem Scheme

• Build the simulation model Real Time interactive platform integrated into a distributed

network model which meets the basic criteria of connection standards.

• Construct a test bench for real time developments using DSSim-PC.

• Test and validate the design of the simulation model in real Time.

2.0.3 Scope

Fully meeting the objectives above, the project will validate one of the Fault Management

Systems and designed with the conditions of Local Network Operator, this being a first step

in co-simulation of Smart Grids in real time, working in the context of Colombia. Proper

integration of the models will be a breakthrough in the understanding of the scheme ADA

Smart Grid, generating knowledge infrastructure of the comprehensive network simulation.


2.0.4 Description of the solution

Prior to the development of designs, it is necessary to explore different methodologies for in-

tegration based on co-simulation models that have been developed, in particular HLA (High

Level Architecture [20] [21]) where interactions between the models correspond to step mes-

sages between objects (defined communication between different components of the Smart

Grid). The configuration information is stored in a file format standard called FED which

in theory should be compatible and portable across the infrastructure, ie that although the

final design includes simulation platforms in various programs, there will be no problems in

compatibility communicate information. Towards developing the solution subsequently pro-

posed by various authors on communications networks to Smart Grids [22] study, identifying

feasible model that includes the basic concepts of quality of service latency and to implement

it. Then, place a first prototype data acquisition over a conventional network (emulation data

flow in one direction) by the simulation software in real time. It is necessary to develop an

environment that integrates the simulation model of the grid (OpenDSS / Labview), shall be

addressed in this model using the technology found in the protective equipment and currently

measuring digital protection relays capable microprocessed provide relevant information for

troubleshooting information. Additionally, you must include the Fault Management System

based on [23] (Matlab-Simulink/Labview) and model of the communication network (Matlab-

Simulink/NS-3) which will be analyzed in conjunction with various failure scenarios power

and their analysis under various circumstances in the communication network. Finally, the

overall environment will be implemented in a NI CompactRIO, evaluating the response of

the models, whose implementation will allow the validation of the integrated communications

platform to Fault Management System.

Chapter 3

Fault Location Algorithm

This chapter describes each block of the Fault Location Algorithm proposed on this study.

The algorithm is designed for radial distribution systems with DG. Importantly, computa-

tional part of the development was based on the proposal of Andres Contreras. The figure

shows the large participation of DSSim-PC for the acquisition of measurements, recognition

of the network as a graph and this was possible the libraries included with the software is

observed. While the algorithm was built and edited in matlab, the user interface is developed

in labview and currently works with the latest version of PC-DSSim.

Were added and improved filters streams not associated with the malfunction, which reduces

computational time and calculating the inverted tree graph arrays mathematically constructed

fault.

Later when you have defined the fault line channel, a vector of decision failure identifies

region blocked knowing the terminal nodes of the system. The latter part of the methodology

remained intact A.Contreras proposal.

10

Chapter 3. Fault Location Algorithm 11

3.1 Algorithm Description

The basis of the method presented on this paper is inspired on [8], where the fault current

with search tree structure is the key to find the faulted zone by a new current amplitude

criteria. The effects of distributed generation brings on the analysis of certain requirements

of the algorithm. In this context, traditional fault location methods need to change because

the problem includes other points of generation which results in a significant impact on the

classical topology of radial distribution network with a single feed point, becoming what [8]

defines as “multi-power region”. A selective protection could solve this problem, but it is

highly limited because it depends on the comparison between positive power direction defined

for the network and the power direction of the fault current, then the algorithm does not

have enough information available, since normally there are not a great number of voltage

transformers. The proposed method requires the current magnitude measurement at the

failure instant, the information is delivered by the IEDs located in each node of the system.

Figure 3.1: Algorithm Blocks [24].

The solution needs a graph study as a model of the distributed power network and a method

to find the optimal shortest-path. This method involves deep inside an undirected Dijkstra

algorithm [25], since there may be current contributions in different ways depending on the


location of the DG, this contributes on solving the problem of compare different power direc-

tions at the fault. According to this, the algorithm flow chart is presented in Fig.3.1 and at

the end of the Chapter Fig.3.9.

3.1.1 Input Data

In order to locate the faulted zone, the algorithm must know the incidence matrix of the

system, which contains a column for each node and one row for each arc. The incidence matrix

is the information core based on the network connection and topology of the distribution

system.

From the incidence matrix, and using the DSSim-PC libraries as it is going to be shown later

on, the algorithm obtains the number of nodes. All system observability is assumed in this

analysis, which means that there is an IED in each node of the system, hence there is a current

magnitude measure for each node.

3.1.2 Current Selection Criteria

At first, the highest current from the three phases is selected for each line in any failure

case (phase failure, single phase, etc). Then non-associated currents are detected and filtered

from the analysis to improve the accuracy of the algorithm. During the development and

programming of the algorithm, it has been identified that there are some current measurements

that could affect its performance. These current values change with the size of the system,

therefore it is not quite functional to set a single value or a limited range of currents. After

several simulation scenarios, the filter criteria is empirically defined as follows:

I > 0, 75 · Imax (3.1)

Line’s currents values that do not meet the condition above, are not going to be part of

the analysis in the algorithm, hence some nodes will not be part of the Fault Line Channel.

This avoids the problems encountered in [8] and solve the disadvantages from [23] about the


comparison criteria associated to the fault currents, providing easy scalability and adaptability

of the algorithm to the system topology as long as the problem involves a radial distribution

system with DG. This criteria could be enhanced by a sensibility study with bigger systems.

3.1.3 Inverted Weights Graph

The optimization problem for the shortest path using undirected Dijkstra’s algorithm requires

the representation of the system as a graph, therefore a previous good calculation of the

incidence matrix is essential in this module, otherwise the fault line channel would lead to

an incorrect faulted zone. First thing to note is that arc weights are related with the current

measurements, then the only chance to follow the shortest path is the inverse of the weights,

so the algorithm reads a significantly high current (fault current) as small distance (inverse

weight) from the shortest path, that is to say a faulted zone. At this point, the methodology

knows the graph of the system, with the inverse of the currents measurements that were not

filtered by the current selection criteria.

3.1.4 Dijkstra’s Algorithm and Fault Line Channel

The “Fault Line Channel” computation is performed once the terminal nodes are identified.

For each terminal node, a minimum distance path is calculated from the main supply node

(slack node) with the inverted weights. Then an undirected Dijkstra’s algorithm is applied to

find the shortest path, it reaches a small zone of the system where a set of nodes are strongly

related to the failure. This idea is quite coherent with [26] where a group of possible faulted

zones are constructed after performing the current classification. However, the methodology

implemented in this study defines a searching size significantly smaller and finally reach to

an accurate “Fault Line Channel”. The algorithm proposed in this paper improves the exe-

cution time because it filters the non-associated currents before computing any process and,

in addition, the algorithm does not include an image reconstruction of the system graph as

[23] neither a picture of the inverse weights system graph which is only used as a illustration

of the mathematical problem.


3.1.5 Fault Decision Vector

Figure 3.2: Fault Decision - Vector G. [24]

A set of nodes from the shortest path is obtained once the undirected Dijkstra’s algorithm

is applied, which means that the identified nodes are strongly associated with the fault. The

Fault Line Channel has the faulted node within or a faulted zone between two nodes, so the

algorithm defines a decision [G] vector to determinate and notify the results to the user.

As shown in Fig.3.2, a value of −1 in G vector means that the fault is located in the zone

between this associated node and the preceding one. Else indicates the last node of the

calculated fault line channel has the failure, or the failure is located on the branch supply line

of this terminal node. Finally the fault location is now done. The program notifies the result

to the user and then the control center takes the next step.

3.2 Labview Program

The main VI Fig.3.3 comprises DSSim-connected PC and acquisition of measurements on all

nodes. In addition, the alarm control is constantly monitored with the average of previous

measurements. This is important because you can start to use the database control centers.

The fault location algorithm is enforceable only when actual measurements of current out of

an expected range, a warning on the screen and then identifies the fault.


Figure 3.3: Main VI - Algorithm

It also has the possibility of generating failures from DSSim-PC or control three-phase faults

from labview on any node. It is important to note that the algorithm works only with

simultaneous failure.

3.2.1 Algorithm’s Process Order

As shown in Fig.3.4, the process will start with the input data:

• Current Magnitudes.

• Nodes.

• Incidence Matrix.

Then. the current filter criteria:

• The highest current of the three phases for each measurement.


• Currents greater than 75% of the largest current. This is to ensure that it is associated

fails.

The condition was given empirically and worth a study of selectivity, however the performance

as discussed in the results is satisfactory.

Diagram of inverted weights. Once you have filtered currents not associated with the fault,

we proceed to reconstruct a graph with weights invested.

Then Dijkstra algorithm and calculation of Fault Line Channel.

Finally, Decision Vector. It decides which is the requested region within the FLC.

Input Data

Data collected fromDSSim-PC using labiewlibraries (Montenegro)

Current Magnitudes

Nodes

Incidence Matrix

Current Selection Criteria

First filter:

I > 0.75 * Imax

Second Filter:

Imax {IA , IB, IC}

Inverted Weights Graph

Arc [i,j]=1/Imax[i,j]

A. Contreras

Dijkstra’s Algorithm and FLC

Undirected Dijkstra’salgorithm to find theshortest path and then the Fault line Channel.

Aryo

Fault Decision Vector

Figure 3.4: Algorithm’s Process Order

3.2.2 Fault Location Routine - Labview

The VI is versatile enough to change Fig.3.5. The input data is easy to find, the algorithm is

located in the labview Matlab Script box. Even the platform becomes a template for future

use proposals DSSim-PC, as in other works you can change the code. Finally the output data

message information that will tell the user the location found.


Figure 3.5: Fault Location Routine

3.2.3 Example: Fault Location Channel IEEE 13 Nodes

A fault is generated at the node 611 of a system 13 including nodes with some wind Generators,

as shown in Fig.3.6. The results of the current magnitudes in Amp is feasible and do a small

test desktop using the ideas and working order algorithm already explained.

Figure 3.6: Fault - Node 611 in DSSim-PC

Although the program does not rebuild graphically cannel fault line - it is not necessary -

mathematically from the matrices that follows a decision vector fault is identified and finally


the desired region. It could be constructed as a desk test like Fig.3.7 and Fig.3.8:

Figure 3.7: Current Magnitudes - Fault at Node 611

Figure 3.8: FLC Example - Fault at Node 611


Figure 3.9: Flux Diagram - Fault Location Algorithm

Chapter 4

Platform Integration in LabVIEW

At first, it is necessary to have a tool which allows to model a Distribution System including

fault’s programming and simulation. Keeping this in mind, OpenDSS commands [27] are

going to be used in order to formulate the electrical model of the distribution systems and

design a fault control simulation on the test cases. Furthermore, one of the most important

actors on this work is DSSim-PC software [28], the non deterministic version of the DSSim-

RT simulator, it is based in the powerful EPRI’s OpenDSS and it is used as a graphical

interface for it. The software arhitecture is based in the actor model (as framework). Some

applications involving OpenDSS and LabVIEW with Real Time analysis have been done with

National Instruments Real Time hardware architectures [29]. Successful results show the

great advantage of this tool for Advanced Distribution Automation (ADA) activities such as

fault location and isolation, feeder reconfiguration, service restoration and Volt-Var control

[30]. According to this, electrical networks can be simulated and faults can be programmed

by using these software.

As shown in Fig. 4.1, the software integration uses DSSim libraries to communicate the

current measurements with the user environment management(link between DSSim-PC and

LabVIEW) which allows the entry of input data to the Fault Location Algorithm designed

in Matlab. Following the previous definitions, the designed fault location algorithm is imple-

mented in LabVIEW, adding an user’s interface. By taking advantage of the compatibility

20

Chapter 4. Platform Integration in LabVIEW 21

DSSim Labview Libraries

user

Figure 4.1: Software Integration Model [24]

between LabVIEW and MATLAB, the algorithm is described in M language. This file in-

cludes the matrix operations and Dijkstra’s algorithm [25] required to execute the program,

allowing fault location in the test systems.

Finally, it is possible to intercommunicate DSSim-PC and LabVIEW through TCP proto-

col [28]. In this manner, LabVIEW’s program consists in acquiring DSSim-PC simulation’s

results, modifying these data to fit the format required by the fault location algorithm and

presenting the user’s controls and indicators on screen. According to this, Fig.4.2 shows the

interface of the designed program for a IEEE 34 nodes test feeder with DG.

Figure 4.2: User Interface - LabVIEW [24]

Chapter 4. Platform Integration in LabVIEW 22

User’s interface presents typical information about the electrical system (e.g., number of

nodes, number of lines, simulation time). It is also possible to read currents magnitudes and

voltages nodes during a fault in the network, because the front panel designed on Labview

allows the user to access a System Monitoring Screen. There is an indicator which shows the

operation status, it would turn out red if a fault appear, or keep on green light if its operation

is normal. A number of controls and indicators allow the user to execute the program and

check the faulted zone. A last fault report is saved on screen with the faulted region and the

time stamp of the fault location, a very important feature for enhancing the database of the

DMS. The Labview application allows the user to co-simulate different fault scenarios from

DSSim-PC or activate them from the front panel, by selecting the node and then generating

or clearing the selected fault.

Chapter 5


Validation

Given the characteristics of the techniques studied. Additional contemplating the require-

ments explained in sections 4, in order to preserve the benefits and that the proposed algo-

rithm completely corrected the disadvantages, defined the design and proposed algorithm for

fault location for distribution systems with DG.

For structuring the final design of the proposed localization algorithm failures was necessary

to keep Long tests and modifications process under different system and simulation scenarios,

then presents the final design of the algorithm.

Applied concepts proposed in [23] algorithm that were used and / or needed modifications

solution required for some of its shortcomings, are presented in the following section.

The decision of faulted area should be on the Interpretation Matrix G ”Fault State Variable

Value”, and with the Dop ”Fault Judgement Matrix” matrix no precise information is obtained

when nodes includes DG. When the fault is in the transmission line and the previous algorithm

does not run, it obtained a solution to this problem executed from the FLC determination.

Is still calculating the matrix ”Fault Judgement Matrix, but only for a verification, determi-

nation of the fault zone can be done on G, so earlier this vector will be the vector

23

Chapter 5. Fault Location Algorithm Validation 24

5.1 Test Cases

In order to check the performance of the fault location algorithm designed, several simulations

are made using IEEE PES Distribution System Analysis Sub-committee’s test systems [31].

5.1.1 IEEE 13 Node Test Feeder

Figure 5.1: IEEE Test case 13 nodes on DSSim-PC [24]

The first test system formulated by IEEE PES Distribution System Analysis Subcommittee

consists of 13 Nodes as showed in Fig.5.1. DSSim-PC’s electrical model was built according

to IEEE’s instructions in [32]. This system does not include DG, but it allows an initial ap-

proximation to the algorithm’s method, and gives the possibility of making a manual tracking

of the fault currents along the network.

Table 5.1 shows the results for all the scenarios simulated.


Second test system formulated by IEEE PES Distribution System Analysis Subcommittee

consists of 34 Nodes as showed in Fig.5.2. DSSim-PC’s electrical model was built according

to IEEE’s instructions in [33]. This system is modified [23] in order to include seven DG

nodes modeled as wind generators.






Finally, third test system formulated by IEEE PES Distribution System Analysis Subcom-

mittee consists of 123 Nodes as showed in Fig.5.3. DSSim-PC’s electrical model was built

according to IEEE’s instructions in [34]. This system is modified [35] in order to minimize

loses due to unsatisfied demand, and includes five DG nodes modeled as wind generators.



Table 5.1: Simulated Scenarios for IEEE Test Case 13 Nodes

No. Fault Type Faulted Node Result

1 Single phase 650 Ok2 Single phase 632 Ok3 Single phase 645 Ok4 Single phase 646 Ok5 Single phase 670 Ok6 Single phase 671 Ok7 Single phase 680 Ok8 Single phase 684 Ok9 Single phase 611 Ok10 Single phase 652 Ok11 Single phase 692 Ok12 Single phase 675 Ok13 Single phase 633 Ok14 Single phase 634 Ok15 Two phase 650 Ok16 Two phase 632 Ok17 Two phase 645 Ok18 Two phase 646 Ok19 Two phase 670 Ok20 Two phase 671 Ok21 Two phase 680 Ok22 Two phase 684 Ok23 Two phase 611 Ok24 Two phase 652 Ok25 Two phase 692 Ok26 Two phase 675 Ok27 Two phase 633 Ok28 Two phase 634 Ok29 Three Phase 650 Ok30 Three Phase 632 Ok31 Three Phase 645 Ok32 Three Phase 646 Ok33 Three Phase 670 Ok34 Three Phase 671 Ok35 Three Phase 680 Ok36 Three Phase 684 Ok37 Three Phase 611 Ok38 Three Phase 652 Ok39 Three Phase 692 Ok40 Three Phase 675 Ok41 Three Phase 633 Ok42 Three Phase 634 Ok




1 Single phase 800 Ok2 Single phase 802 Ok3 Single phase 806 Ok4 Single phase 808 Ok5 Single phase 810 Ok6 Single phase 812 Ok7 Single phase 814 Ok8 Single phase 850 Ok9 Single phase 816 Ok10 Single phase 818 Ok11 Single phase 820 Ok12 Single phase 822 Ok13 Single phase 824 Ok14 Single phase 826 Ok15 Single phase 828 Ok16 Single phase 830 Ok17 Single phase 854 Ok18 Single phase 856 Ok19 Single phase 852 Ok20 Single phase 857 Ok21 Single phase 832 Ok22 Single phase 888 Ok23 Single phase 890 Ok24 Single phase 858 Ok25 Single phase 864 Ok26 Single phase 834 Ok27 Single phase 842 Ok28 Single phase 844 Ok29 Single phase 846 Ok30 Single phase 848 Ok31 Single phase 860 Ok32 Single phase 836 Ok33 Single phase 862 Ok34 Single phase 838 Ok35 Single phase 840 Ok36 Two phase 814 Ok37 Two phase 858 Ok38 Two phase 840 Ok39 Two phase 848 Ok40 Two phase 856 Ok



41 Two phase 890 Ok42 Two phase 822 Ok43 Two phase 826 Ok44 Two phase 854 Ok45 Two phase 832 Ok46 Three phase 858 Ok47 Three phase 848 Ok48 Three phase 890 Ok49 Three phase 826 Ok50 Three phase 832 Ok




1 Single phase 150 Ok2 Single phase 1 Ok3 Single phase 8 Ok4 Single phase 14 Ok5 Single phase 6 Ok6 Single phase 18 Ok7 Single phase 20 Ok8 Single phase 25 Ok9 Single phase 250 Ok10 Single phase 33 Ok11 Single phase 47 Ok12 Single phase 51 Ok13 Single phase 46 Ok14 Single phase 41 Ok15 Single phase 35 Ok16 Single phase 39 Ok17 Single phase 131 Ok18 Single phase 64 Ok19 Single phase 60 Ok20 Single phase 610 Ok21 Single phase 95 Ok22 Single phase 83 Ok23 Single phase 88 Ok24 Single phase 79 No25 Single phase 75 Ok26 Single phase 71 Ok27 Single phase 450 No28 Single phase 114 Ok29 Single phase 111 Ok30 Single phase 97 Ok31 Single phase 92 Ok32 Single phase 56 Ok33 Single phase 59 Ok34 Single phase 107 Ok35 Single phase 104 Ok36 Two phase 450 Ok37 Two phase 14 Ok38 Two phase 25 Ok39 Two phase 51 Ok40 Two phase 39 Ok



41 Two phase 610 Ok42 Two phase 95 Ok43 Two phase 79 Ok44 Two phase 100 Ok45 Two phase 97 No46 Three phase 56 Ok47 Three phase 86 Ok48 Three phase 79 Ok49 Three phase 75 Ok50 Three phase 107 Ok

Chapter 6

Heterogeneous Platform: Design

and Construction

This chapter focuses on the platform design. It is planned for connection to a power amplifier

accuracy where the signal of one of the nodes and in cases of current signals will be generated

will be handled with an electronic load, connect it to a real IED and interact with system

models from a distribution whose robustness cRIO hardware and Labview software ensures

reliable performance.

Based on the fault location system designed, it is possible to propose a hardware-software ar-

chitecture that takes advantage of its capabilities and extends its scope. This test bench could

include a solid integration between LabVIEW and DSSim-PC using the well programmed li-

braries that came with the free distribution system software, and the making a full operation

along with new hardware elements.

For example, fault location algorithm described in LabVIEW and M language would be

executed in a NI CompactRIO embedded system, meanwhile the distribution test system

configuration and simulation would be done in a desktop computer. Communication between

these two modules can be achieved by using an Ethernet connection. Up next, the hardware

requirements and programming structure are explained, then a basic fault scenario is shown.

31

Chapter 6. Heterogeneous Platform: Design and Construction 32

Power Amplifier

Electronic Load

IED

Test System NodeNI cRIO

DistributionTest System

OpenDSS

DSSim

LabVIEW - CRio


MATLAB

LabVIEW

PC

Communication Model

Matlab/Simulink -

OPNET

Figure 6.1: Prototype of the Hardware-In-The-Loop Test Bench proposed. [24]

6.1 Hardware Requirements

The main objective of the platform is to simulate one or more system’s nodes status (voltage

and current) from the electrical system, to interact in different fault scenarios with real elec-

trical hardware such as IEDs, PMUs, etc. In order to implement the test bench, the following

power signals are needed:

• Voltage of the required node after the PT.

• Current of the required line after the CT.

Since a real IED have two control inputs by default and needs at least the recloser status

monitor, the following control/digital signals should be included in the platform:

• Trip control signal.

• Close control signal.

• Open recloser status signal.

• Close recloser status signal.


Figure 6.2: Constructed Test Bench

The project is done by using a precision power amplifier NF 4510 [36] connected to a cRIO

9082 [37], the interface must work with digital inputs module NI 9205, digital outputs module

NI 9269 and analog outputs module NI 9206. An electronic load NF AS-513 [38] can be used

to generate current measurements in this node’s IED. A first prototype shown in Fig.6.1.

Finally, its status flag can be connected back to a digital input, hence giving information

about the required node. By controlling current through the electronic load, the user will

be able to generate faults in the node, and test bench will operate accordingly. Keeping this

in mind, Fig.6.2 shows the implemented prototype of the Hardware-In-The-Loop test bench

proposed based on all the specifications explained before.

In order for a new user to work with the designed fault locator, it is possible to run test

systems of any size, taking advantage of the program’s scalability. LabVIEW and DSSim-PC

integration allows an implementation of new algorithms and codes that improve the fault

location performance. These auxiliary locating methods can be based in different techniques,

keeping in mind the compatibility with DSSim-PC’s analysis and results.

6.2 Software Programming

At first it is overriding to identify two players of this design: The electrical system modeled

in DSSim-PC, and the functional algorithm process to take control decisions over it. Putting


both of them in a same loop for this application it is not quite right because the information

processing will not be able to add any additional data until the first piece of data is done

processing.

A Producer/Consumer design Labview pattern [39], based on the Master/Slave pattern is

used to integrate the multiple elements shown in Fig.6.1, where the data producer would be

DSSim-PC and the consumer loop is going to be the functional algorithm to enhance data

sharing between them, because they are actually running at different rates although in a

parallel working paradigm.

Figure 6.3: Software Structure: Producer - Consumer Scructure

In Fig. 6.3 it is shown the basic template for this test bench software pattern. The upper time

loop is where the DSSim-PC data system is produced, it is commonly used when acquiring

multiple sets of data, in this case the platform will take: Voltages, currents, switch status, and

all the information suitable for the user application in order to be processed. This information

goes to a global variable.

The first time loop initializes the TCP/IP connection, data is generated and then the con-

nection is closed every time rate. The second time loop represents the FPGA interface which

works consuming the data produced in the upper loop, since the FPGA works at a different


rate compared with DSSim-PC, it is important to calibrate time loops to decrease the possible

time delays on the amplification process.

The programming structure allows to queuing up (producing by DSSim-PC) this data much

faster than the actual processing (consuming), that is the reason why the Producer/Consumer

design pattern is a great alternative for this application. The Producer/Consumer pattern

approaches to this application allows to co-simulate electrical systems modeled in DSSim-PC

to a real time hardware-in-the-loop environment.

6.3 Fault Scenario: A Basic Example

The following case is modeled and implemented in order to evaluate the test bench perfor-

mance at a glance.

Figure 6.4: Basic Test Example

On table 6.1 the system information is shown at the end of this chapter. It is important to

mention that the second load is 6.5 times higher than the first one, so in case of a three phase

fault on node N 6 would cause an over current in that line, while the voltage goes to zero for

a time lap. Switch 3 will protect the load LD 2 in that scenario, and when it is disconnected

the voltage profile will increase in the main bus. The Switch SW 2 will protect the first load

of this over voltage.


The test case is a basic system of a tractor with two branches, each with their shields. The

load of the upper branch is 18KW and another load is 72 KW 4 times greater than the first.

The exercise aims to recreate a scenario simulating a three-phase ground fault at node 6,

then will be disconnected in order to exercise scheduled for overcurrent protection. With this

burden off the voltage profile will rise a little and the next overvoltage protection is activated

by disconnecting both loads.

6.3.1 RT-HIL Data Consistency

The platform begins to validate in order to corroborate the proposed HIL. In principle, verify

that both voltage signals and power are recreated in the ranges stipulated and understood

by IEDs according to the provisions of the simulation Fig.?? In this case it is abstracting the

line current and 227.83 Amp meter reading IED (BECO 7679) is 227.20 for the third phase,

which is an error of 0.27%.

Figure 6.5: RT-HIL Data Consistency: Amplification

An important tool for assessing the consistency of data in this case is to compare the pro-

grammed parameter in curve FDI and recorded from the electrical data disturbance. The

relationship between the operating current and the fault current to the previous case is 4.45.


The FDI will be scheduled with overcurrent function is the SEL 751A with a sequence of 4

trips using a TCC C5 extreme reverse curve with a time delay factor of 0.05. As can be seen

in Fig.6.6 from the selected curve is expected for a single overcurrent time scale for the trip

close to 45 mSec. According to the disturbance event for this trip was sent to the 48 mSec (3

cycles) and open time not exceeding 10 cycles complying with regulations to avoid damaging

an actual recloser.

Iop = 570 Amp

If =2537 Amp

Scale = 4.45

Time (TCC 0.05) = 45 mSec

Time (IED Oscilography) = 48 mSec - 3 Cycles

User Manual SEL 571A

Figure 6.6: RT-HIL Data Consistency:TCC and Trip times


Table 6.1: Basic Electric Case Information

Description

System

Phases=3 basekv=21 pu=1.05Angle=30 Frequency=60Isc3=300 Isc1=250R1=1.65 1.65 1.65 BaseFreq=60

Transformer

Phases=3wdg=1Conn=delta KV=19 Kva= 1000 %r=0.55 XHL=50 XLT=1 XHT=1

wdg=2Conn=wye KV=0.208 Kva= 500 %r=0.55

Line 1 Linecode=mtx602 Length=0.0186 Phases=3Line 2 Linecode=mtx601 Length=0.0186 Phases=3

Switch 1 Transformer Protection

Switch 2Real Time HIL Connection - BECO 7679Over Voltage ProtectionANSI 27 3P

Switch 3Real Time HIL Connection - SEL 751AOver Current ProtectionANSI 51 3P - 81 Recloser Trip Sequence

Load 1Phases=3 Kv=0.120 Kw=18Pf=0.95 Model=1 Basefreq=60

Load 2Phases=3 Kv=0.120 Kw=72Pf=0.95 Model=1 Basefreq=60

Chapter 7

Results Discussion and Conclusions

This chapter shows the results of both parts of this study: The fault location algorithm and

the test bench operation.

7.1 Results Discussions

After having defined three different systems for testing purposes, it is possible to demonstrate

the scalability of the fault location algorithm by running several simulations for each network.

It is desired that no matter the type of the fault or the test system’s size; the program can

successfully find the faulted zone. Each simulated scenario is a different fault type on a

different node, so this information will help to calculate the accuracy of the method.

7.1.1 Fault location on IEEE 13 nodes

At first, a single phase fault is simulated at node 670 of the IEEE 13 Node Test Feeder.

Figure 7.1 shows the results after programming the fault and having executed the algorithm

in LabVIEW. The fault locator successfully informs that Node 670 is faulted and the FLC

corresponds to FLC = [650, 632, 670]. To evaluate the performance of the algorithm plus the

accuracy, different cases of simulations varying the fault type (single phase, double phase to

ground and three phase fault) and the faulted node in the test feeder are made.

39

Chapter 7. Results Discussion and Conclusions 40

Figure 7.1: IEEE Test case 13 nodes Fault Programming and Location Result. [24]

Table 7.1 presents the obtained results after 42 scenarios with 100% of accuracy for IEEE 13

nodes test case.

7.1.2 Fault location on IEEE 34 nodes with DG

Then, working with the IEEE 34 Node Test Feeder, a phase to phase fault is simulated

between A and B phases at node 860. Figure 7.2 shows the results after programming the

fault and having executed the algorithm in LabVIEW. The fault locator successfully informs

that Node 860 is faulted and the FLC corresponds to FLC = [800, 802, 806, 808, 812, 814,

850, 816, 824, 828, 830, 854, 852, 832, 858, 834, 860].


Table 7.1 presents the obtained results after 50 scenarios, 35 cases for single phase fault, 10

cases for double phase to ground and 5 cases for three phase fault with 100% of accuracy.


7.1.3 Fault location on IEEE 123 nodes with DG

Finally, swapping to the IEEE 123 Node Test Feeder, a three phase fault is simulated at

node 76. Figure 7.3 shows the results after programming the fault and having executed the

algorithm in LabVIEW. The fault locator successfully informs that Node 76 is faulted and

the FLC corresponds to FLC = [150, 149, 1, 7, 8, 13, 152, 52, 53, 54, 57, 60, 160, 67, 72, 76].


Table 7.1: Test Feeder Results [24]

Faut Type (#) Scenarios Accuracy (%)

IEEE 13 NodesSingle Phase 14 100Double Phase 14 100Three Phase 14 100



Table 7.1 presents the obtained results after 50 scenarios, 35 cases for single phase fault with

94% of accuracy, 10 cases for double phase to ground with 90% of accuracy and 5 cases for

three phase fault with 100% of accuracy. It is important to note here, that the accuracy level

slides down a bit because, the system lose its radial topology in two simulations for the single

phase fault where the fault was programmed on nodes 79 and 450, hence the fault location

algorithm does not work; the same situation on the double phase to ground fault simulation


where the fault was located on the 97 node but the algorithm did not answer the right faulted

zone.

In general, for the 123 Nodes Test Feeder, the algorithm performance was successful in 47 over

50 simulations, except the three cases where the system gets a mesh topology consequence of

the switches configuration.

After several tests were done, fault locator’s performance has been proved for test systems

of different sizes. Its accuracy has improved since error sources have been filtered by the

algorithm. However, it is important to clarify that in order for the algorithm to work properly,

distribution systems must be totally radial. Problems have been found when fault current

has more than one branch between source and faulted node.

Figure 7.4: Current Line A - Harmonics

Figure 7.5: Voltage Line A - Harmonics


Figure 7.6: NormalOperation IED Oscilography

Figure 7.7: Over Current Fault - IED Oscilography

Figure 7.8: Trip for Reclose Trial - IED Oscilography

7.1.4 Heterogeneous Platform performance

It is important that the amplified voltage and current signals keep free from any possible dis-

tortion. The signals are reproduced with exact 59.99 Hz of frequency with the voltage/current

test case ranges, measured from two different IEDs and an oscilloscope. Fig. 7.4 shows how

the current of Line A after the precision amplifier NF 4510 has a maximum THD of 1.2% at

the 4th harmonic.


Figure 7.9: Successful Reclosing

On the other hand, 7.5 refers the voltage signal at the output of the amplifier, obtained from

the IED SEL 751A on the same line. As can be seen, the rate of harmonic distortion THD is

3.11%, slightly above the minimum standard. This is consequence of the voltage amplification

module which could be solved using a three phase precision amplifier voltage.

It is important to mention that control signals are used by digital inputs and digital outputs

from the IED. Although the monitoring PC is connected by ethernet using TCP/IP, or DNP3.

The platform allows to extract signals after the voltage or current PT CT after a power

system modeling DSSim-PC on a node or line of interest by applying hardware / software

architectures that allow integration HIL and connecting with real control devices . The

tests were performed with two different models used in industry to control reclosers: BECO

7679 and SEL 751st. The platform has been successfully used to develop thesis projects of

undergraduates in different areas of the ADA as:

• Evaluation of protection features IEDs [40].

• Adaptive Protection [41].

• System Reconfiguration [42].

• Volt / Var control in the latter application a student project developed its specialization

using a relay to control BECO capacitor banks [43].

As shown below in Fig.7.10, there is a list of protection functions within which were evaluated

and tested 5 of them in the test bed is presented, also a future scope is shown. The other is


capable of being worked on the same platform with the tools that are already open and can

give new projects in automation.

Functions• 81 Frequency – FUTURE SCOPE

• 59 Overvoltage – DONE

• 47 Neg. Sequence Overvoltage – FUTURE SCOPE

• 27 Under voltage – DONE

• 67 Ground protection – FUTURE SCOPE

• 51 Inverse time overcurrent – DONE

• 50 Breaker failure – DONE

• 25 Sync check – DONE

• PQ metering – FUTURE SCOPE

• Harmonics (BECO)– FUTURE SCOPE

Figure 7.10: Protection Function Tested and Future Work

7.1.5 Basic case: Oscilograph results

A event series are programmed in DSSim-PC for the Basic system 6.4 on the following se-

quence:

• System at normal operation without faults Fig.7.6.

• System with 3-phase fault at node N 6 Fig.7.7.

• System with 3-phase fault at node N 6 still and a Trip pulse trial from the IED Fig.7.8.

• Second IED reclosing (Only voltage signal) Fig.7.9.

The second event above in Fig.7.7 shows the instant of the fault. The first cycles are of the

last moment of a normal operation and then near to the 24th cycle, and over current appear

reaching the 2000 A expected from the co-simulation model status. at the same time the


voltage signal falls down to zero and this stays for 8 cycles. In a real IED this is coherent

because the recloser could be at a fault state for 10 cycles maximum. The trip pulse appears

2 cycles after the overcurrent pickup (IEC C5 extremely inverted for an ANSI 51P).

The delay after the trip pulse is caused by the signal generation and amplification, but it does

not exceed the limits of a real case.

7.2 Conclusion

The paper proposes a fault location method for radial distribution systems with distributed

generation. Advanced distribution automation technologies and mainly data sources are suit-

able to improve fault location perform by associating the nodes measurements to inverse arc

weights, and then the algorithm solves and optimization problem to find a shortest path of the

network interpreted as a graph, using power system models as presented. It is the first fault

location algorithm which works with DSSim-PC integrated with MATLAB and LabVIEW,

to show how the advanced distribution automation technologies such as IEDs help to reach

an accurate fault location result.

Three examples illustrate how adaptable and scalable the fault location methodology could

be by programming different failures in random nodes of the systems. The method proposed

allows achieving better accuracy by taking advantage of radial distribution systems topology,

and filtering non-associated currents to the failure since the current selection criteria works

with the maximum magnitude of the fault current measurements.

The hybrid platform implemented works with the prototype designed, it is the first test bench

which co-simulates distribution system models on DSSim-PC connected with real IEDs. The

voltage and current signals are well done taken to a Hardware-In-The-Loop architecture with

coherent results of a basic fault scenario. The IEDs response to the ANSI programmed pro-

tections are stable, and approaches the environment for different ADA smart solutions and

methodologies besides fault location, such as adaptive protection experiments, reconfigura-

tion algorithms and Volt/Var control. These functions have been already explored by four

undergraduate thesis using the test bench reviewed in this study.


The test bench is not only for research and academic utilities, there is a further work on

protective and relaying coordination planning, that could be a great help to improve real

systems, enhance the studies and recreate fault scenarios to measure multiple impacts of the

power grid, therefore to make better decisions before any implementation.

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Documents

Heterogeneous Platform for IEDs Connection on a Fault