IJDACR

ISSN: 2319-4863

International Journal of Digital Application & Contemporary research

Website: www.ijdacr.com (Volume II, Issue 1, July 2013)

Modelling & Simulation of SMIB Using TCSC by PSO and

Genetic Algorithm

Surya Prakash Joshi Jitendra BIkaneria Naveen Sen sryjoshi@yahoo.com jitendra.bikanera@gmail.com naveen87_sen@yahoo.com

Abstract: The single Machine Infinite Bus is a model used

to represent the problems of the power system. The

model is simulated using Matlab language. With the

faults in transmission lines there is a deviation in the

rotor speed of generator accusing errors in the system.

Further, when the line is cleared and all faults are

exterminating the fault effects still keep their significance

presence in the form of rotor speed. In this thesis to

overcome this problem we have generated a feedback

system known as TCSC Controller. This one input

system is supplied with the deviation signal, to measure

the required voltage change to simulate back speed of

rotor. Further the parameters defining the working

methodology of TCSC are required to be monitored.

With the marginal values the preferred output is precise

to its reference but the time consumption by the scheme

is not up to the industrial standards. Hence to simulate

the working of TCSC the parameters are defined by two

algorithms i.e. Genetic Algorithm and Particle Swarm

Optimization. The mathematical model and its Matlab

implantation are developed in this project. The results

and simulation of outcome better than the previous

technologies could be verified with the results.

Keywords: TCSC, SMIB, PSO, GA

I. Introduction

Power systems now are equipped larger systems

with EHV (extra high voltage), miles of

transmission and also inter-regional networking.

With the advancement in the socioeconomics, the

methods of modern transmission grid

management and operations also upgraded with

needs, there is also the demand of its security,

stability, high efficiency, and flexible operational

control has dramatically integrated, so

developing new means of regulation to enhance

its controllable is a constant subject of study for

researchers. Thyristor controlled series capacitor

(TCSC) is a system that came into existence from

their conventional parents i.e. fixed series

capacitor. The effective fundamental equivalent

reactance can be regulated periodically by

controlling the thyristor in a relatively large range

that can be either capacitive or inductive. As a

novel method for electrical network control,

TCSC can be utilized in the power system

transient stability enhancement, power system

oscillation damping, the SSR mitigation and load

flow control [1].

II. Functioning of FACTS

TCSC is one of the important FACTS devices

that have the capability to vary the apparent

impedances of a given transmission source.

TCSC constitutes of three components- a

capacitor bank C, a bypass inductor L and the

bidirectional thyristors SCR1 and SCR2 as shown

in Fig.1 [2, 3]. In Fig.1 Ic and IL represents

instantaneous values of the capacitor bank and

inductor respectively Is is the instantaneous

current of the controlled transmission line, V is

the instantaneous voltage across the TCSC. The

firing angle (α) of the thyristors is controlled to

adjust the TCSC reactance. The TCSC can be

controlled to work in capacitive zone. The

equation of reactance which is function of (α) is

represented by equation (1).

XTCSC (α) = XC - 𝑋𝐶2

𝑋𝐶−𝑋𝐿 𝜎+𝑠𝑖𝑛(𝜎)

𝜋

4𝑋𝐶2

𝑋𝐶−𝑋𝐿 cos2(

𝜎

2)

𝐾2−1

(𝐾𝑡𝑎𝑛(𝐾𝜎

2)−tan(

𝜎

2))

𝜋 (1)

XC= Nominal Reactance of Fixed Capacitor C.

XL= Inductive Reactance of Inductor L connected

in parallel with C.

σ= 2(π-α) = Conductance angle of TCSC

Controller.

IJDACR

IJDACR

ISSN: 2319-4863

International Journal of Digital Application & Contemporary research

Website: www.ijdacr.com (Volume II, Issue 1, July 2013)

K= √𝑋𝐶

𝑋𝐿 = Compensation ratio.

Figure 1: TCSC Circuit Diagram

III. Single Machine Infinite Bus

The Single Machine Finite State system can

qualitatively demonstrate the significant

properties in the behavior of a multi-machine

system and is convincingly simple to examine.

Henceforth it is conventionally accepted for

describing the universal concepts of power

system stability, influence of arbitrary factors

upon stability and alternative concepts of

controllers.

IV. Modeling of Power System

The SMIB power system powered with TCSC

(shown in Fig.2), is considered in this study. Here

a synchronous generator propagates power to the

infinite-bus via double circuit transmission line

and a TCSC. The Fig. 2, depicts that Vt and Eb are

referred as the generator terminal and infinite bus

voltage respectively; XT, XL and XTH represents

the reactance of the transformer, transmission

line per circuit and the Thevenin’s impedance of

the receiving end system respectively. The state

equations may be written as equations [4].

ω = [𝑃𝑚−𝑃𝑒−𝐷(𝑤−1)]

𝑀 (2)

δ = ωb (ω-1) (3)

The state equations may be written as [5]

𝑑𝛿

𝑑𝑡 = wB (Sm – Smo) (4)

𝑑𝑆𝑚

𝑑𝑡 =

1

2𝐻 [-D (Sm – Smo) + Tm – Te] (5)

𝑑𝐸′𝑞

𝑑𝑡 =

1

𝑇′𝑑𝑜 [-𝐸𝑞

′ + (Xd – x’d)id + Efd] (6)

𝑑𝐸′𝑑

𝑑𝑡 =

1

𝑇′𝑞𝑜 [-𝐸𝑑

′ + (Xq – X’q)iq] (7)

The electrical torque Te is expressed in terms of

variables E'd , E' q , id and iq as:

Te = E’d id + E’q iq + (x’d + x’q)id iq (8)

For a lossless network, the stator algebraic

equations and the network equations are

expressed as:

E’q +x’d id = vq (9)

E’d – x’q iq = vd (10)

Vq = -xeid + Evcosδ (11)

Vd = xeiq - Ebsinδ (12)

Solving the above equations, the variables id and

iq can be obtained as:

Id = 𝐸𝑏𝑐𝑜𝑠𝛿−𝐸𝑞

′

𝑋𝑒+𝑋𝑑′ (13)

Iq = 𝐸𝑏𝑠𝑖𝑛𝛿−𝐸𝑞

′

𝑋𝑒+𝑋𝑞′ (14)

V. Particle Swarm Optimization

Particle swarm optimization (PSO) method is one

of the optimization techniques and a kind of

evolutionary computation technique. The method

has been found to be robust in solving problems

featuring nonlinearity and non differentiability,

multiple optima, and high

IJDACR

IJDACR

ISSN: 2319-4863

International Journal of Digital Application & Contemporary research

Website: www.ijdacr.com (Volume II, Issue 1, July 2013)

Figure 2: Flow Chart of Particle Swarm Optimization

dimensionality through adaptation, which is

through adaptation, which is derived from social-

psychological theory. It is a population based

search algorithm [6].

In PSO every particle struggles to improve them

self by copying traits from their successful peers.

Also, each particle is enabled with a memory and

hence is capable of acknowledging the finest

place in the search space ever visited by it. The

position that corresponds to the best fitness is

recognized by the term pbest and the overall best

out of all the particles in the population is called

gbest [7].

The adjustment of the particle’s position can be

mathematically modeled according the following

equation:

Vik+1 = wVi

k +c1 rand1(…) x (pbesti-sik) + c2

rand2(…) x (gbest-sik)

where, vik : velocity of agent i at iteration k,

w: weighting function,

cj : weighting factor,

rand: uniformly distributed random number

between 0 and 1,

sik: current position of agent i at iteration k,

pbesti : pbest of agent i,

gbest: gbest of the group.

The following weighting function is usually

applied in above equation

w = wMax-[(wMax-wMin) x iter]/maxIter

where wMax= initial weight,

wMin = final weight,

maxIter = maximum iteration number,

iter = current iteration number.

sik+1 = si

k + Vik+1

Initialize particles with random position and velocity vectors.

Loo

p u

ntil m

ax iter

Loo

p u

nti

l all

par

ticl

es e

xhau

st

Start

If fitness (p) better than fitness (pbest) than pbest=p

Set best of pBests as gBest

Update particle velocity and position

Stop: giving gBest, optimal solution.

For each particle’s position (p) evaluate fitness

IJDACR

IJDACR

ISSN: 2319-4863

International Journal of Digital Application & Contemporary research

Website: www.ijdacr.com (Volume II, Issue 1, July 2013)

VI. Problem Formulation

The SMIB System works fluently in the transient

state. If the associated transmission line recieves

zero error during its process the machine would

provide a error-free output and will continue in

synchronization with the input. The rotor of the

generator machine experiences a deviation in its

motion when it gets a fault in any of the phase.

These faults could be the outcome from a random

malfunctioning of any device. Further, on

clearing the line and eradicating the fault

condition the speed deviation of rotor continues

to exist in the machine. Hence to maximize the

efficiency and to overcome the un-necessary

objections countering with the machine a

feedback scheme is installed in parallel with the

system. The scheme here for the study is

Thyristor Controlled Series Compensator

(TCSC).

TCSC has only one input. The deviation of the

motor is provided to this end. A generator exciter

initiates a signal with the reference of this input

value, to define the voltage value required to

simulate the rotor velocity of generator.

The value of the parameters of a TCSC Controller

should be appropriate. In the else condition, with

the marginal value the rotor velocity of generator

would be in required synchronization but the

process will be time consuming. Hence we have

incorporated two algorithms i.e. Genetic

Algorithm and Particle Swarm Optimization to

control the parameters of TCSC.

VII. Results and Simulations

Figure 3: Speed Deviation in SMIB with Fault

@ 5 sec with No TCSC

Figure 4: Speed Deviation in SMIB with Fault

@ 5 sec with Nil, GA and PSO- TCSC

VIII. Conclusion and Future Work

A. Conclusion

The TCSC system implemented in MATLAB

efficiently improves the performance of a Single

Machine Infinite Bus System (SMIB). The results

show the order of improvement in the system.

IJDACR

IJDACR

ISSN: 2319-4863

International Journal of Digital Application & Contemporary research

Website: www.ijdacr.com (Volume II, Issue 1, July 2013)

The deviation introduced by the transmission line

and its effect on motor are a serious threat on the

performance. TCSC voltage regulation abilities

have paced the motor to a constant speed despite

the fluctuations in the line. Also the parameters of

this system are studied in this project for the

maximum efficiency. The two algorithms

Genetic and Particle Swarm Optimization have

reduced the time consumption of TCSC which

was a drawback feature in the conventional

TCSC. The system is tested on MATLAB and the

results verify the proposed work.

B. Future Scope

The SMIB model provides its applications in the

industries in a variety of formats. Every industry

using this scheme has its own set of disciplines

for operations. The SMIB is configured

according to the needs and thus the deviation in

its speed depends on intellectual conditions. The

TCSC presented here is a model of general

scheme. A better version could be a demand of

future with the upcoming technologies. Also a

variety of algorithms can provide the model a

stable feature for the physical conditions of a

particular industry.

IX. References

[1] P. Sunilkumar., “Transient Stability Enhancement of

Power System Using TCSC,” International Journal of

Electrical and Computer Engineering (IJECE) Vol.2, No.3,

June 2012, pp. 317~324 ISSN: 2088-8708

[2] H.F.Wang, F.J.Swift, FACTS- Based stabilizer designed

by the phase compensation method part I on single machine

power systems, Advances in power system control,

operation and management, 1997. APSCOM-97, Fourth

international conference on 11-14 Nov 1997.

[3] Ch. Koteswara Rao, Y.Rambabu, S.Radha Krishna

Reddy, G. Poorna Chandra Rao, B. Sarveswara Reddy, “A

Novel Technique on Thyristor Controlled Series

Compensator Based Genetic Algorithm Controller to

Improve Stability of Single Machine Infinite Bus System”.

International Journal of Engineering Research and

Development e-ISSN: 2278-067X, p-ISSN: 2278-800X,

www.ijerd.com Volume 3, Issue 4 (August 2012), PP. 47-51

[4] Ali Yazdekhasti, Iman Sadeghkhani, “Optimal Tuning of

TCSC Controller Using Particle Swarm Optimization”.

Advances in Electrical Engineering System 24 Vol. 1, No. 1,

March 2012 Copyright ©World Science Publisher, United

States

[5] Yu, Y.N., Power System Dynamics, Academic press

Inc., London (1983)

[6] N.Srikanth, Atejasri, “Enhancing Power System Stability

by Using Thyristor Controlled Series Compensator”.

International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622 www.ijera.com

Vol. 2, Issue 5, September- October 2012, pp.1817-1824

[7] Swathi Kommamuri & P. Sureshbabu, “Optimal

Location and Design of TCSC controller For Improvement

of Stability”. International Journal of Instrumentation,

Control and Automation (IJICA)ISSN : 2231-1890 Volume-

1, Issue-2, 2011

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