Maximum Power Point Tracking for PV System Using Advanced

International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 2, Issue 12, December 2012)

58

Maximum Power Point Tracking for PV System

Using Advanced Neural Networks Technique A. M. Zaki, S. I. Amer, M. Mostafa

Electronics Research Institute (ERI), Dokki, Cairo, Egypt.

Abstract- Solar energy is clean, renewable and its

decentralized character is appropriate well at the scattered

state of the zones with low density of population. The cost of

electricity from the solar array system is more expensive

than the electricity from the utility grid. So, it is necessary

to operate the PV system at maximum efficiency by

tracking maximum power point at any environmental

condition. In this work, the neural network back

propagation algorithm is used to control the operation of

the PV array in order to extract the maximum power. Two

error functions are used. The first, the classic error

function, and the second is a modified error function which

takes into consideration the derivative of the error function

also. The results obtained are compared and discussed.

Keyword- Solar Energy, PV System, Maximum Power

Point Tracking (MPPT), Neural Networks, Modified Error

Function

I. INTRODUCTION

The world is increasing experiencing a great need for

additional energy resources so as to reduce dependency

on conventional sources, and photovoltaic (PV) energy

could be an answer to that need. Renewable energy

becomes an essential source for many applications in the

last four decades. It is difficult to supply electrical energy

to small applications in remote areas from the utility grid

or from small generators. Stand alone photovoltaic (PV)

systems are the best solutions in many small electrical

energy demand applications such as communication

systems, water pumping and low power appliances in

rural area. In addition, solar energy is clean, renewable

and is used where it is and its decentralized character is

appropriate well at the scattered state of the zones with

low density of population. Consequently, it can

contribute to the environmental protection and be

regarded as an alternative with a future to conventional

energies. There are two ways to generate electricity from

sun; through photovoltaic (PV) and solar thermal

systems. Generally, PV systems can be divided into three

categories; stand-alone, grid-connection and hybrid

systems. For places that are far from a conventional

power generation system, stand-alone PV power supply

system has been considered a good alternative.

In order to overcome the undesired effects on the

output PV power and draw its maximum power, it is

possible to insert a DC/DC converter between the PV

generator and the batteries, which can control the seeking

of the MPP, beside including the typical functions

assigned to controllers. These converters are normally

named as maximum power point trackers (MPPTs) [1].

The cost of electricity from the solar array system is

more expensive compared to electricity from the utility

grid. For that reason, it is necessary to study carefully the

efficiency of the entire solar system to design an efficient

system to cover the load demands with lower cost. There

are many external and internal influences which have an

effect on the efficiency of the PV panel. A robust control

using a PI regulator is used to track this maximum power

point. The PI regulator used to control the boost DC/DC

converter is synthesized by frequency synthesis using

Bode method. For that, they have developed a transfer

function of global mode using a small signal method [2].

An intelligent artificial technique to determine the

maximum power point (MPP) based on artificial neural

network is detailed. The approach is compared to perturb

and observe (P&Q) method. The MPPT using artificial

neural network proposed can reduce the noises and

oscillations generated by classical methods and can be

competitive against other MPPT algorithms [3]. Other

researchers presented a method for the control of the PV

system through the MPPT using Fuzzy Logic controller.

This method succeeded to reduce the PV array area and

increase their output, and used for control of MPPT for

stand-alone PV system givning a minimum economic

cost. Developed controller can be improved by changing

the form of the functions of memberships as well as the

number of subsets [4]. Also a neuro fuzzy controller

(NFC) is proposed to track the MPP. It takes advantage

in conjunction with the reasoning capability or fuzzy

logical systems and the learning capability of neural

networks. A gradient estimator based on a radial basis

function neural network is developed to provide the

reference information to the NFC. With a derived

learning algorithm, the parameters of NFC are updated

adaptively.



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The NFC is initialized using the expert knowledge

from the traditional fuzzy control, which reduces the

burden of the lengthy pre-learning with a derived

learning algorithm , the parameters in the NFC are

updated adaptively by observing the tracking errors. A

radial basis function neural network (RBFNN) is

designed to provide the NFC with gradient information,

which reduces the complexity of the neural system [5].

The Adaptive Neuro-Fuzzy Inference System (ANFIS)

has recently attracted the attention of researchers in

various scientific and engineering areas. The ANFIS is

designed as a combination of the surgeon fuzzy model

and neural network. The fuzzy logic controller (FLC)

utilizes the ANFIS output voltage to track the MPP, to

acquire high efficiency with low fluctuation [6]. The

modeling of a photovoltaic power supply PVPS-system

using an ANFIS, was presented in [7]. For the modeling

of the PVPS-system, it is required to find suitable models

for its different components (ANFIS-PV-array, ANFIS-

battery and ANFIS-regulator) under variable climatic

conditions. Test results provided that the ANFIS

performed better than the neural networks. The results

obtained from ANFIS can also be used for the prediction

of the optimal configuration of PV systems, for the

control of PV systems and for the prediction of the

performance of the systems. Intelligent control technique

using fuzzy logic control is associated to an MPPT

controller in order to improve energy conversion

efficiency and this fuzzy logic controller is then

improved by using genetic algorithms (GA) [8].

In this paper, a MPPT for PV array algorithm based on

Neural Networks technique is presented. Section 2 shows

the PV cell equivalent circuit. Section 3 presents the PV

array characteristics and section 4 presents the Artificial

Neural Networks feed-forward algorithm. The error

function used is shown in the classic form and also a

modified error function. The Neural Network used in this

work is presented in section 5. Section 6 shows the

results and the discussion, while section 7 gives the

conclusions of the work.

II. PV CELL EQUIVALENT CIRCUIT

The maximum operating point of solar photovoltaic

(PV) panels changes with environmental conditions. The

maximum power point (MPP) of a PV system depends on

cell temperature and solar irradiation, so it is necessary to

continually track the MPP of the solar array. Many

methods have been proposed to locate and track the

maximum power point (MPP) of PV cells. The

difficulties that face these methods are the rapid changes

in solar radiation and the variety in cell temperature

which affects the MPP setting.

External sensors are used in many approaches to

measure solar irradiation and ambient temperature to

estimate the MPP as a function of data measured.

A solar cell basically is a p-n semiconductor junction.

When exposed to light, a dc current is generated which

varies linearly with the solar irradiance. Figure (1) shows

the equivalent circuit of the PV cell [9].

Fig.(1) Equivalent circuit of PV cell

The (I-V) characteristics of the PV model is described

by the following equation:

( ( ) )

(1)

Where:

I cell current (A)

IL light generated current (A)

ID diode saturation current (A)

q charge of the electron = 1.6*10-19

(coul)

K Boltzman constant (j/k)

T cell temperature (k)

Rs cell series resistance (ohms)

Rsh cell shunt resistance (ohms)

V cell output voltage (V)

III. PV ARRAY CHARACTERISTICS

As it is crucial to operate the PV energy conversion

systems at or near the maximum power point to increase

the PV system efficiency, the study of the PV array

characteristics became of great importance. The

nonlinear nature of PV system is apparent from figure

(2). In addition, the maximum power operating point

varies with insolation level and temperature. Therefore

the tracking control of the maximum power point is a

complicated problem. Hence, the use of intelligent

control techniques such as Artificial Neural Networks

(ANNs) have gained great popularity to solve this

problem [10].



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Fig. (2) Solar radiation & ambient temperature influence on the I-

V& P-V characteristic [11].

IV. ARTIFICIAL NEURAL NETWORKS

Due to increasing need for artificial intelligence

techniques, such as neural network, they have been

recently widely applied on industrial electronics, and

have a large perspective in intelligent control area that is

evident by the publications in the literature. However, the

literature in this area is hardly more in a last decade.

Neural Network itself is a vast discipline in artificial

intelligence, and the basic technology has advanced

tremendously in recent years. Recently intelligent based

schemes have been introduced [3-7].

Artificial Neural Networks try to mimic the biological

brain neural networks into mathematical models. From

two decades, artificial neural network captivates the

attention of a large number of scientific communities. It

has been advancing rapidly and its applications are

expanding in different areas.

A The Feed-forward Neural Network

Figure (3) illustrates an example of a feed-forward

neural network (FFNN). The output of the neuron unit

can be expressed as:

( ) (∑ ( ) ) (2)

Where:

wi weight of connection

Ɵ bias of the neuron unit

N number of inputs to the neuron

t time

xi input to the neuron



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Fig. (3) Feed-forward neural network

The FFNNs have been applied successfully to solve

some difficult and diverse problems by training them in a

supervised manner with a highly popular algorithm

known as the Back Propagation (BP) algorithm [12]. The

BP training algorithm is an iterative gradient algorithm

designed to minimize the mean square error between the

actual output of a feed-forward net and the desired

output.

B Neuro-Controller with a Modified Error Function

The back propagation algorithm is based on the

steepest descent method. The algorithm is therefore

stochastic in nature, i.e. it has a tendency to zigzag its

way about the true direction to a minimum on the error

surface. The error function used in the BP algorithm is as

follows:

Error = Vref – Vout (3)

It suffers from a slow convergence property. To

improve the neuro-controller performance in the on-line

training mode, a modified error function was proposed

[13]:

Error = (Vref – Vout )- k1*(d Vout /dt) (4)

Where k1 is constant.

V. NEURAL NETWORK USED CONFIGURATION

The neural network used in this work is shown in

figure (4). It consists of three layers. Three inputs in the

input layer, four nodes in the hidden layer and one output

constitutes the output layer.

Fig.(4) Neural Network Configuration

The block diagram of the control system is presented

in figure (5).

Fig.(5) Control System Block Diagram

According to the array temperature and the solar

irradiation values, the maximum power point voltage

Vmax is selected from the look-up table and input to the

neural network as a reference voltage Vr. The output of

the neural network is input to the solar array model to

calculate the array voltage. The output is fedback as

previous value to the input Vo-1

. The system was tested

using MATLAB M-file.

VI. RESULTS AND DISCUSSION

The neural network control system presented in this

work was tested using the solar array data shown in the

following table and results obtained are presented in

Table(1) .



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Table1.

MSX-60 MODULE (SOLAREX COMPANY)

Temp

T

(G) Irrid

25oc

50 oc

75 oc

100 oc

125 oc

0

0

0

0

0

0

0.1

13

9.2

6

2.8

0.16

0.2

14

10

6.8

3.6

.36

0.3

14.6

10.9

7.4

4.3

1.6

0.4

15

11.3

7.8

4.5

1.8

0.5

15.2

11.6

8.2

4.8

2

0.6

15.5

11.8

8.43

5

2.32

0.7

15.6

12

8.6

5.3

2.4

0.8

15.9

12.2

8.7

5.5

2.6

0.9

16

12.4

8.9

5.6

2.76

1

16.12

12.5

9

5.7

2.8

The results obtained in the three cases show that the

neural network with modified error function shows better

convergence rate. The output reaches the reference value

in few iterations. While using the classic error function

takes longer time to reach the reference maximum

voltage target. So, the modified error function gives

greatly better results during on-line control.

Fig.(6) Reference Value at (T=50 , G= .2).

Fig.(7) Reference Value at (T=25 , G= .2).

Fig.(8) Reference Value at (T=25 , G= 1) .

VII. CONCLUSIONS

In this work, a neural network control system for

Maximum Power Point Tracking (MPPT) for a PV array

was presented. The back propagation neural network was

used with the error calculated in its usual form and also

with a modified error function which takes into

consideration the derivative of the error. The two cases

were tested and results were compared. Although both

cases give good results, the modified error function

resulted in faster convergence rate which give superiority

in case of on-line control of the PV array.

REFERENCES

[1 ] V. Salas, E. Olias, A. Barrado and A. Lazaro, "Review of the maximum power point tracking algorithms for stand-alone

photovoltaic systems", Solar Energy Materials & Solar Cells 90

(2006), pp. 1555-1578.

[2 ] M. Salhi and R. El-Bachtiri, " Maximum Power Point Tracking

Controller for PV systems using a PI regulator with Boost DC/DC

converter", ICGST-ACSE journal ISSN 1687-4811,Vol.8, issue iii, January 2009, pp. 21-27.



63

[3 ] M. Hatti, A. Mcharrar, M. Tiourci, " Novel approach of Maximum

Power Point Tracking for Photovoltaic Module Neural Network Based", International Symposium on Environment Friendly

Energies in Electrical Applications EFEEA'10, 2-4 November

2010, Ghardaia, Algeria, pp.1-6.

[4 ] A. Mellit, M. Benghanme, A. Hadj and A. Guessoum, " Control

of Stand-Alone Photovoltaic System Using Controller", Proc. Of Fourteenth Symposium on Improving Systems in Hot and Humid

Climates, TX May 17-20, 2004.

[5 ] LI Chun, ZHU Xin, SUI Sheng and HU Wan, "Maximum Power Point Tracking of a Photovoltaic Energy System Using Neural

Fuzzy Techniques", J Shanghai Univ (Engl Ed), 2009, 13(1), pp.29-36.

[6 ] Abdulaziz M, S. Aldobhani and Robert John, " Maximum Power

Point Tracking of PV System Using ANFIS Prediction and Fuzzy

Tracking", Procs. Of the Inter. Multi Conf. of engineers and

Computer Scientists 2008, vol. II, IMECS 2008, 19-21 March,

Hong Kong.

[7 ] A. Mellit and S. A Kalogirou,"ANFIS-based Modeling for a

Photovoltaic Power Supply System: A case Study", ELSEVIER, Renewable Energy 36 (2011), pp.250-258.

[8 ] C. Larbes, S.M. Ait Cheikh, T. Obeidi and A. Zerguerras, "

Genetic Algorithms Optimized Fuzzy Logic Control for the Maximum Power Point Tracking in Photovoltaic System",

Renewable Energy 34(2009) pp. 2093-2100.

[9 ] M. Azab, " A New Maximum Power Point Tracking for

Photovoltaic Systems", Inter. National Journal of Electrical and

Electronics Engineering, Vol. 3, No. 11, 2003, pp. 702-705.

[10 ] N. Patchara., S. Premrud. And Y. Sriuth., " Maximum Power

Point Tracking using Adaptive Fuzzy Logic Control for Grid-

Connected Photovoltaic System", Elsevier Publishing ltd, 2005.

[11 ] S. Premrud. And N. Patana.," Solar-Array Modelling and

Maximum Power Point Tracking Using Neural Networks", IEEE Bologna Power Tech Conference, June 23th-26, Bologna, Italy.

2003

[12 ] W. T. Miller, S. R. Suttin and P. J. Werbos, " Neural Network for

Control", MIT Press, Cambridge, MA, 1990.

[13 ] M. M. Salem, A. M. Zaki, O. A. Mahgoub, E. Abu-El-Zahab and O. P. Malik, "On-Line Trained Neuro-Controller with a Modified

Error Function", Canadian Conference on Electrical and

Computer Engineering, Habfax, May 7-10, 2000.

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Maximum Power Point Tracking for PV System Using Advanced