A Study of Coordinated TCSC and PSS Damping Controller in a Multi Machine System using PSO To Improve Power System Stability

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A Study of Coordinated TCSC and PSS Damping Controller in a Multi Machine System using PSO To Improve Power System Stability

Ankita Runani1, Dr. Y. S Brar2

1M.Tech student, Guru Nanak Dev Engg. College, Ludhiana, India

2Professor, Guru Nanak Dev Engg. College, Ludhiana, India

Abstract This paper investigates a robust simultaneous design technique of PSS and TCSC damping controller in order to mitigate the power system oscillations. The design problem is formulated as an optimization problem and an objective function is created. PSO algorithm is used to minimize the objective function and find out the optimal values of controller gains and the time constants. Time domain simulations are carried out in MATLAB/ Simulink and the coordinated system is tested in multi machine system.


  1. PSS Modeling [1], [6]

    Fig 1. Shows the transfer-function model of lead-lag based controller structure of PSS. The stabilizing signal of PSS can be given as:

    = 1+1 1+3 (1)

    Results reveal that the power system oscillations are damped out with coordinated control of PSS and TCSC in a multi machine system.





    Keywords FACTS devices, Power system stabilizer, Power system stability, Particle swarm optimization, Thyristor controlled series capacitor .


    The power system is continuously is being subjected to a variety of disturbances. Large disturbances include severe lightning strokes, loss of transmission lines carrying bulk power due to overloading or voltage collapse due to heavy load or sudden increase in demand of reactive power. The power system must be capable of withstanding the disturbance either small or large and must operate satisfactorily under these conditions and thus meet the load demand. A variety of power electronics based devices known as flexible ac transmission system devices (FACTS) offer high speed, flexibility and reliability. TCSC is one of the FACTS devices that plays an impotant role in power systems such os optimal control of power flow, providing series compensation, removing sub-synchronous resonance (SSR), damping out oscillations thereby enhancing the power system stability. The main purpose of Power system stabilizer (PSS) is to sense changes in the generatot output power and by scheming the excitation value it reduces the power swings in the system. But PSS is not capable of damping out the oscillations in a multi machine power system alone, so in this work a robust coordinated design of PSS and TCSC damping controller has been proposed whose controller parameters are optimized using a population based algorithm known as Particle Swarm Optimization (PSO). A typical two machine system is considered for investigating the performance of PSS and TCSC. A MATLAB simulation is carried out to demonstrate the performance of PSS and TCSC in mitigating power system oscillations and thereby improving the transient stability of the power system.

    = Stabilizer gain,

    = Washout time constant,

    1 , 2 , 3 , 4 = Time constants of lead-lag network of PSS.

    The washout block in the controller structure, is used to reduce the over-response of the damping during severe events. It is a high pass filter(HPF) with purpose to respond only to oscillations in speed and block the dc offsets. A washout time of 10 sec or less is recommended to quickly remove the low frequency oscillations.

    Fig 1 Excitation system model with AVR and PSS

  2. TCSC Modeling [1], [9]

Fig 2. Shows the transfer-function model of lead-lag based controller structure of TCSC. The stabilizing signal of TCSC can be expressed as:

and 2 are the positive cognitive and social components that are held for varying the particle velocity towards pbest and gbest respectively, 1 and 2 are random numbers generated in range of [ 0-1]. The inertia weight is responsible for adjusting the velocity of the particles and is updated by following equation:

= 1+1 1+3 (2)








= Gain of TCSC

= Washout time contant

Fig 2 Structure of Lead-Lag based TCSC Controller


Particle Swarm Optimization (PSO) is an efficient, robust and simple optimization technique developed by Kennedy and Eberhart and is used in solving various problems in power systems. PSO is a stochastic population based optimization technique that is derived from socio-psychological theory. This technique is inspired by flock of birds circling over an area where they can smell their source of food. The bird who is closest to the food chirps the loudest and other birds tend to move in his direction. It is a computational method that tries to improve a candidate solution iteratively with a given measure of quality. The position and velocity vectors og the ith particle in D-dimensional space can be given as = 1, 2, . . and =

1, 2, respectively. In each iteration particles will update their velocities and positions by using following quation:

Where, is maximum no. of iterations and iter is present no. of iteration. [3]


    The selection of TCSC damping controller and PSS parameters is a complex optimization problem. A performance index based on system dynamics is formulated and is used as an objective function for the design problem. In this study, the main work is to minimize an objective function given by :


    = = 1 2 (6)

    Where, 1 and 2 are the measured speed and the reference speed and is the time range of the simulation. The design problem can be formulated as following optimization problem: [1], [5]

    1 1 1

    2 2 2

    3 3 3

    4 4 4

    1 1 1

    2 2 2

    3 3 3

    4 4 4

    Where, and are the gains of PSS and TCSC respectively.

    The optimization of the TCSC controller parameters is carried out by evaluating the objective cost function.


    Two-machine five-bus system is taken as the test system to analyze the dynamic performance of the power system. The

    , +1

    = , +1

    + 1 1


    , +

    analysis of power system transient stability has been done using PSS and TCSC. The model of a 2-machine 5-bus system has been

    22 , , (3)

    , +1 = , +1 + , +1 (4)

    Where, , +1 and , +1 represent the velocity vector and the position vector of the ith particle at iteration iter, , and

    , are the personal best position of the ith particle and

    global best position of swarm at iteration iter. The constants 1

    designed in Simpower System Toolbox of MATLAB. A three phase fault has been applied on the phase A of the transmission line between bys 1 and bus 2 at t= 1sec and cleared after 5 cycles.

    The values of the optimized controller parameters are shown in the following tables:

    TABLE I Optimized values for Time constants of PSS after using


    Time constants

    Optimized values using










    TABLE II Optimized values for Time constants of TCSC after using PSO

    Time constants

    Optimized value after

    using PSO









    TABLE III Optimized values for Heffron Phillips constants of PSS after using PSO

    Heffron Philips constants

    Optimized values using PSO













    TABLE IV Optimized values for Gains of PSS and TCSC after using PSO

    Gain of PSS and


    Optimized values

    after using PSO

    KA (KPSS)


    KT (KTCSC)


    It has been observed that when no compensation is provided, then on occurrence of fault, the system becomes unstable and amplitude of waveform keeps on increasing. When the compensation is provided to the system in terms of PSS and TCSC, it has been observed that the oscillations decrease in amplitude with time and the system becomes stable.

    Fig.3 Variation of output voltage with PSS and TCSC

    Fig. 4 Variation of Line power with PSS and TCSC

    Fig 5 Rotor Angle deviation with PSS and TCSC


In this thesis, a vigorous design technique for coordinated control of Power System Stabilizer (PSS) and Thyristor Controlled Series Capacitor (TCSC) has been discussed. The PSSs and TCSC design problem is formulated as an Optimization problem and thus an Objective function is minimized using Particle Swarm


The Heffron Phillips constants are given by:

K1= change in electrical power at constant flux, with change in load angle.

Optimization (PSO) in order to find the optimal controller

= =


+ 0 cos 0

parameters. The robustness of proposed scheme is carried out in a multi machine power system. The simulation analysis and from




0 +

different waveforms give us result that the test system dynamic performance is improved and the system employing PSS and TCSC exhibits lower oscillations and thus enhance power system stability.

K2= change in electrical power at constant load angle with change

in field flux.

2 = = sin 0



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    K3= impedance factor

    = = +

    TCSC based controller for power system damping, IEEE International conference on Power Electronics, December




  2. Gurala, Sen, Power System Stabilizers Design for

    K4= demagnetizing effect of the armature reaction

    Interconnected Power Systems, IEEE Transactions on

    = =


    Power System, May 2010.

  3. J. Kennedy, and R. Eberhart, Particle Swarm Optimization,


    + 0

    IEEE International Conference on Neural Networks, vol. 4, November 1995.

  4. K.R Padiyar FACTS Controllers in Power Transmission and

    K5= change in terminal voltage at constant load angle, with change in field flux.

    Distribution, New age international publishers, 2007.

    = = 0 cos


  5. Khormizi, Nia, Damping of Power system Oscillations in Multi Machine Power System Using Coordinate Design of



    0 + 0

    PSS and TCSC, IEEE Transactions on Power Systems, 2011.

  6. P.Kundur Power System Stability and Control, Power System Engineering Series (Mc Graw-Hill, New York, 1994).

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    International Electrical Engineering Journal, Vol.3, 2012

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  9. Morsali, Rasool Coordinated design of MPSS and TCSC- based Damping Controller using PSO to enhance Power System Stability, IEEE Transactions, 2013.

K6= change in terminal voltage at constant flux, with change in

load angle.


= =


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