Automatic Generation Control for an Interconnected Reheat Thermal Power Systems Using Wavelet Neural Network Controller

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 4, April 2012)

406

Automatic Generation Control for an Interconnected Reheat

Thermal Power Systems Using Wavelet Neural Network

Controller

R.Francis

1

,

Dr. I. A.Chidambaram

2

1_{Asst. Professor,}2_{Professor , Electrical Engg, Annamalai University, Annamalai Nagar, India.}

1_{[email protected]} 2_{[email protected]}

Abstract -This paper presents optimization of integral controller gains of the two area interconnected thermal power system with the fast acting energy storaging devices. The energy storing device especially Redox Flow Batteries (RFB) can efficiently damp out electromechanical oscillation in power system because of their efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The proposed controller is designed with the feasibility of applying a wavelet neural network (WNN) approach for the automatic generation control and implemented in a two area interconnected thermal power system without and with RFB. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% step- load disturbance in area 1 were obtained. The present intelligent control system trained the wavelet neural network (WNN) controller on line with adaptive learning rates. The comparison of frequency deviations and tie-line power deviations for the two area interconnected thermal power system without and with RFB reveals that the system with RFB enhances a better stability than that of system without RFB.

Keywords - Automatic generation control, Integral square error criterion, Redox flow batteries, wavelet neural network.

I. INTRODUCTION

Automatic generation control is a very important criterion in electric power system design and operation. It is well known that the main objectives of AGC in multi-area power systems are to keep the tie-line power flows in a prescribed tolerance and to fix the frequency of each area within limits. Designing load frequency controllers has received great attention of researchers in recent years, and many control strategies have been developed [1-6]. The integral controller was one of control strategy, which is still widely used nowadays in industry, although it

gives large frequency deviations due to the nonlinearities present in the power system. A linear model can be obtained by linearizing the differential equations describing the dynamic performance of the power system around an operating point.

However, power system loads are usually variable according to the load demand. Several adaptive control techniques have been suggested to overcome these

shortcomings of the conventional techniques. The main

feature of such control is to extend the stability margin of the power systems.

The artificial intelligence neural network and fuzzy

logic control approaches have been applied

successfully to the controllers used in load frequency

control. Such intelligent control systems are

independent of the power system mathematical model parameters, but they can work with the available system time responses. The wavelet neural network approach has been applied successfully to the two area interconnected thermal power systems. The proposed controller provided very fast response relative to that of the fixed gain controllers. The proposed wavelet neural network (WNN) load frequency controller has a very good and fast tracking control performance relative to that of the conventional neural network technique without prior knowledge of the controlled plant [11-14]. The stabilization of frequency oscillations in an interconnected power system became challenging when implemented in future competitive environment. The inclusion of redox flow batteries in the interconnected power system is to enhance a quality and reliable power system. The redox flow batteries (RFB) are found to be superior over the other energy storing devised because of its

 Operability at normal temperature.

 Small temperature changes.

 Small loss during stand by and ends a long

service life.

 Flexibility in layout.

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International Journal of Emerging Technology and Advanced Engineering

407

II. TWO AREA INTERCONNECTED REHEAT POWER

SYSTEM WITH RFB UNITS

The interconnected two area power system is considered to be divided into two control areas that are connected by tie lines. This is optimized and added with Redox Flow Batteries.

A. Integral gain optimization

The fixed gain controllers which are designed at nominal operating conditions and fail to provide best control performance over a wide range of operating conditions. But it is desirable to keep system performance near its optimum. Thus, the objective function of the optimization of the performance of the system [15] is given in equation (2.0).

.

(2.0)

B. Modeling of two area interconnected power systems with RFB

The linearized mathematical model of the two area (thermal–reheat) power system, is given by the following set of the state variable differential equations as



( ) . ( ) ( ) ( )



1

)

( 1 1 1 1

1 1

1 Pg s Kc Pc s Pd s Ptie s

sTps Kps s

F     

   (2.1) ) ( 1 1 ) ( 1 1 1 1

1 Pt s

sTr Tr sKr s

Pg  

    (2.2) ) ( 1 1 ) ( 1 1

1 Xe s

sTt s t P      (2.3)      _ _ _  

 ( ) 1 ()

1 1 ) ( ₁ 1 1 1

1 F s

R s Pc sTg s Xe (2.4)



( ) ( )



. 2 )

( 1 2

12 _F _s _F _s

s T s

Ptie   

  (2.5)



( ) . ( ) ( ) . ( )



1 )

( 2 2 2 2 12

2 2

2 Pg s Kc Pc s Pd s a Pties

sTps Kps s

F      

   (2.6) ) ( 1 1 ) ( ₂ 2 2 2

2 Pt s

sTr Tr sKr s

Pg  

    (2.7) ) ( 1 1 ) ( 2 2

2 Xe s

sTt s t P      (2.8)      _ _ _  

 ( ) 1 ( )

1 1 ) ( 2 2 2 2

2 F s

R s Pc sTg s Xe (2.9)

The system state space equations are developed as



X

= Ax+ Bu +d (2.10)

Where, x, u and d are the state, control and disturbance vectors. The control and disturbance vectors are given by:

System Control input vector _

               2 1 2 1 c c P P u u u (2.11) Disturbance vector                 2 1 2 1 D D P P d d

d (2.12)

Where ,

Augmented system matrix

       A C A 0

0 _(2.13)

Augmented control input matrixB



0 B



(2.14)

Augmented disturbance matrix 



0 



(2.15)

Augmented output matrix C



0 C



(2.16)

Two state vectors ∫ ACE1 and ∫ ACE2 are in clouded

in the augmented state matrix and eleven state variables are presented in this augmented form.

∫ACEi = ∫βi ∆fi +∆Ptie i=1, 2 (2.17)

Substituting the value we get,

(2.18)

C. RFB UNITS in LFC

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International Journal of Emerging Technology and Advanced Engineering

408

Fig-2 BLOCK DIAGRAM OF A TWO – AREA INTERCONNECTED POWER SYSTEM WITH REDOX FLOW BATTERIES

The RFB modeling equations are

(2.20)

i

rfbi Kci Pc

P  

 .

(2.21)

III. WAVELET NEURAL NETWORK CONTROLLER

The structure of a four layer wavelet neural network

(WNN) controller [11-14] is shown in Fig.3.The

objective of the control problem is to track the frequency deviation to zero in the case of a load disturbance. To achieve this control means, the frequency deviation is taken as a tracking error, e.

i i i

rfbi Pc

sTd Kc

P 

  

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International Journal of Emerging Technology and Advanced Engineering

409

The input of the WNN consists of the error e and e(1 –

z-1), where z-1 is the time delay, and the output of the WNN

is the control input signal U, which represents Up1, Up2. A. Description of the WNN approach

The signal propagation and the basic function in each layer can be described as follows. For every node i in the input layer, the net input and the net output can be described by the following equations:

---- (3.1)

where x11=e and x21 = e(1 – z-1 ). Moreover, the family

of wavelets is usually constructed by

translation and dilations performed on a single fixed function, called the mother wavelet.

In each layer, each node performs a wavelet Uj that is derived from its mother wavelet. The first derivative of a Gaussian function is adopted as a mother wavelet in this

study and expressed as ф(x) = -x exp(-x2

/2). For the jth

node,

-- (

3.2)

--- (3.3)

Where mij and σij are the translation and dilation in the

jth term of the ith input xi2to the node of the mother wavelet

layer, and n is the number of wavelets with respect to each input node k.

Each node k in the wavelet layer is denoted by П, which multiplies the input signals and outputs the result

of the product. Hence, for the kth rule node,

--- (3.4)

Where x3j represents the jth input to the node of the

wavelet layer, and w3jk the weights between the mother

wavelet layer and the wavelet layer, which are assumed to be unity. The number of the wavelet is given as l = n/i if each input node has the same mother wavelet nodes. The Σ is known as the single node 0 in the output layer, which computes the overall output as the summation of all its input signals:

--- (3.5)

Where the connecting weight w4 ko is the output

action strength of the oth output associated with the kth

wavelet, and x4k represents the kth input to the node of

the output layer and y41 =U. Hence, the output signal

given by the formula represents the controller signal as follows:

--- (3.6)

The same procedure can be applied for ΔUp2.

IV. SIMULATION RESULTS AND OBSERVATIONS

The integral controllers are designed and

implemented in the two area interconnected power system with and without RFB and also applied wavelet

neural network in the interconnected reheat

power

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International Journal of Emerging Technology and Advanced Engineering

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V. CONCLUSIONS

An intelligent wavelet neural network (WNN) controller (LFC) is proposed to control the frequency of the two area interconnected thermal–reheat power systems. The integral gains are optimized by the simulation procedures adopted in the two area interconnected thermal reheat power systems. The proposed WNN controller is implemented in a two area interconnected power systems and design implementation of the controller is found that the

frequency and tie-line power deviations of the

interconnected power systems with WNN controller ensures improved transient response of the system.

ACKNOWLEDGEMENT:

The authors wish to that the authorities of Annamalai University, Annamalai nagar-608002, Tamilnadu, India for the facilities provided to prepare this paper.

Appendix-1

Data for the interconnected two area thermal power system.

Rating of each area=2000 MW Base power=2000 MVA

f= 60 Hz

R1=R2= 2.4 Hz/Pu MW

Tg1 = Tg2=0.08 sec

Tt1 = Tt2=0.3 sec.

Tp1 =Tp2= 20 sec

Kp1 = Kp2=120 Hz/Pu MW

B1=B2= 0.425 Pu MW/Hz

T12 = 0.545 MW/Hz

ΔPd1=0.01pu MW/HZ

a12 = -1.

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International Journal of Emerging Technology and Advanced Engineering

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0 5 10 15 20 25 30

-0.15 -0.1 -0.05 0 0.05 0.1

Time (s)

ch

an

ge

in

fre

q.i

n a

rea

1

Integral W NN

0 5 10 15 20 25 30

-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03

Time (s)

ch

an

ge

in

fre

q.

in

are

a 2

Integral W NN

Fig5: Frequency Deviation of area1 and area2 in a two area interconnected thermal reheat power system with integral and WNN controller for 1% step load disturbance in area1

0 5 10 15 20 25 30

-9 -8 -7 -6 -5 -4 -3 -2 -1

0x 10

-3

Time (s)

Tie

Li

ne

P

ow

er

Integral W NN

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International Journal of Emerging Technology and Advanced Engineering

412

0 5 10 15 20 25 30

-6 -5 -4 -3 -2 -1

0x 10

-3

Time (s)

Ti

e

Li

ne

P

ow

er

Integral with RFB WNN

Fig7: Tie-line power Deviations in a two area interconnected thermal reheat power system integral and WNN controller for 1% step load disturbance in area

REFERENCES

[1 ] Shayeghi.H, Shayanfar.H.A, Jalili.A,”Load frequency control stratergies: A state of the art survey for the researcher”, Energy conservation and Management.2009; 50(2); 344-353.

[2 ] Ibraheem.I, Kumar.p and Kothari.D.P,”Resent philosophies of automatic generation control statergies in power systems”.IEEE Transaction on Power Systems 2005; 20(1):pp346–357.

[3 ] Malik OP, Kumar A, Hope GS. “A load frequency control algorithm based on a generalised approach”. IEEE Transaction on Power System 1988; 3(2):375–82.

[4 ] Velusami.S, Chidambaram.IA,”Decentralized biased dual mode controller for LFC of interconnected powersystems considering GDB and GRC nonlinearities”, Energy conversion & Management.2007; 48(1):1691-1702.

[5 ] N.Jaleeli.N,Vanslyen.LS,Ewart.DN,.Fink LH,”Understanding automatic generation control”, IEEE Transaction on Power Systems 1999; 7(3):1106–1122.

[6 ] J. Nanda, B.L. Kaul,”Automatic generation control of an interconnected power system”, IEE Proc. Gener. Transm. Distrib. 125 (5), (1978).

[7 ] Pan CT, Liaw CM.” An adaptive controller for power system load frequency control”. IEEE Trans Power Syst 2001; 4(1):122–128. [8 ] Wang, Zhou, Wen. “Robust-load frequency controller design for

power systems”. IEE Proc C 1993; 140(1):11–16.

[9 ] Janardan Nanda, Ashish Mangla, Sanjay Suri,”Some new findings on AGC of an interconnected hydrothermal system with conventional controllers”, IEEE Transaction on Energy Conversion,2006;21(1):187-194.

[10 ]Enomoto K, Sasaki T, Shigematsu T, Deguchi H,”Evaluation study about redox flow battery response and its modeling”. IEEE Transaction on Power System Engg, 2002; 122-B (4):554–560.

[11 ]M. Thuillard, “A review of wavelet networks, wavenets, fuzzy wavenets and their applications”, ESIT 2000 Aachen, Germany, 2000, pp 5–16.

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Appendix-2 List of symbols: f Frequency

Kp Power system gain

Kr Reheat thermal power system gains

Tr Reheat time constants

Tt Time constant of turbine

Xe Governor Valve position

Tg Time constant of governor

Pg Turbine output power

R Regulation parameter

Tij Tie-line synchronizing coefficient

aij Operator

Tp Power system time constant

Pref The output of ACE

X State vector A, B State matrices

Δfi Frequency deviation of area i (i = 1, 2)

ΔPGi Generation deviation of area i (i = 1, 2)

ΔXEi Governor Valve position deviation of area i

(i = 1, 2)

ΔPtie Tie line power deviation of area i (i = 1, 2)

ΔPdi Step load input of area i (i = 1, 2)

ΔPri Rotor position deviation of area i (i = 1, 2)

ΔUpi Controller signal deviation of area i (i = 1, 2)

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International Journal of Emerging Technology and Advanced Engineering

413 Biographies

R.Francis (1977) received

Bachelor of Engineering in Electrical and Electronics Engineering (1999), Master of Engineering in Power System Engineering (2001) from Annamalai University, Annamalainagar.From 2002 he is working as Assistant professor in the Department of Electrical Engineering, Annamalai University. He is a member of ISTE. His research interests are in Power Systems and Electrical

Measurements and Controls. (Electrical Machines

Laboratory, Department of Electrical Engineering,

Annamalai University, Annamalainagar –

608002,Tamilnadu,India) [email protected]

I.A.Chidambaram (1966) received

Bachelor of Engineering in Electrical and Electronics Engineering (1987), Master of Engineering in Power System Engineering (1992) and PhD in Electrical

Engineering (2007) from Annamalai University,

Annamalainagar. During 1988 - 1993 he was working as Lecturer in the Department of Electrical Engineering, Annamalai University and from 2007 he is working as Professor in the Department of Electrical Engineering, Annamalai University, Annamalainagar. He is a member of ISTE and ISC. His research interests are in Power Systems,

Electrical Measurements and Controls. (Electrical

Measurements Laboratory, Department of Electrical Engineering, Annamalai University, Annamalainagar – 608002, Tamilnadu, India, Tel: 9104144238501, Fax:

-91-04144-238275) [email protected],