The paper investigates the use of Thyristor Controlled Series Capacitor (TCSC) for improving small signal stability and managing congestion in heavily loaded power systems. Utilizing Particle Swarm Optimization (PSO), it determines the optimal locations and parameters for TCSC installation to enhance system oscillation damping and alleviate line flow congestion. The findings indicate that strategically placed TCSC controllers can effectively mitigate small signal issues and enhance power flow management in multimachine power systems.

1. ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 03, October 2011
Small Signal Stability Improvement and Congestion
Management Using PSO Based TCSC Controller
D. Mondal1, A. Chakrabarti2 and A. Sengupta3
1
Haldia Institute of Technology, Department of Electronics and Instrumentation Engineering, Purba Medinipur,
Haldia-721657, India. (E-mail: mondald12@yahoo.in)
2, 3
Bengal Engineering and Science University, Department of Electrical Engineering, Howrah-711103, India
(E-mail: a_chakraborti55@yahoo.com and sgaparajita@gmail.com )
Abstract— In this paper an attempt has been made to study the employed as the first choice to mitigate these problems.
application of Thyristor Controlled Series Capacitor (TCSC) However, performance of PSS gets affected by network
to mitigate small signal stability problem in addition to configurations, load variations etc. Hence installations of
congestion management of a heavily loaded line in a
FACTS devices have been suggested in this paper to achieve
multimachine power system. The Flexible AC Transmission
System (FACTS) devices such as TCSC can be used to control
appreciable damping of system oscillations.
the power flows in the network and can help in improvement But when TCSC has been installed at different locations,
of small signal stability aspect. It can also provide relief to its performance and effect is different. It is important to locate
congestion in the heavily loaded line. However, the TCSC devices at suitable place in the transmission network
performance of any FACTS device highly depends upon its to achieve desired objectives. The optimal allocation of TCSC
parameters and placement at suitable locations in the power has been reported in literatures [5]-[7] based on many aspects
network. In this paper, Particle Swarm Optimization (PSO) - optimal power flow (OPF) with lowest cost generation,
method has been used for determining the optimal locations reactive power planning, etc. In this paper the TCSC has
and parameters of the TCSC controller in order to damp small
been used for small signal stability improvement and a novel
signal oscillations. Transmission Line Flow (TLF) Sensitivity
method has been used for curtailment of non-firm load to
stochastic method, Particle Swarm Optimization [8] has been
limit power flow congestion. The results of simulation reveals implemented for finding the optimal location and parameters
that TCSC controllers, placed optimally, not only mitigate of the TCSC controller. Though PSO has been employed in
small signal oscillations but they can also alleviate line flow several research papers [9]-[10] for the design of optimal
congestion effectively. FACTS controllers, the applications are mostly limited to the
case of single machine infinite bus system.
Keywords—Congestion management, Particle Swarm In this paper the application of TCSC controller has been
Optimization, Small Signal Stability, Thyristor Controlled extended to study the small signal oscillation problem in case
Series Compensator, Transmission Line Flow Sensitivity.
of a multimachine power system and a novel use of TCSC
has been explored for transmission overload curtailment and
I. INTRODUCTION
congestion management. Transmission Line Flow (TLF)
In deregulated power industry congestion occurs more sensitivity values for the most overloaded line have been
frequently on many individual transmission systems when considered for calculating the necessary load curtailment for
contingency or load increase happens in power system and the alleviation of the transmission congestion.
it may even cause some physical violations such as The paper has been organized as follows: section II de-
transmission line overload or low/high bus voltage profile in scribes the definition and method of (TLF sensitivity based)
the system. In these cases, it may be needed to shed some congestion managements. The small signal modeling of the
load to ensure that transmission overload security limits are multimachine system working with TCSC controllers has been
satisfied. The development of FACTS has potential represented in section III. In section IV the general theory of
contribution on load curtailment issues [1]. Thyristor PSO and the proposed optimization problem has been dis-
Controlled Series Compensator (TCSC) is a series controlled cussed. The impact of TCSC on load curtailment has been
device in the FACTS family that are increasingly applied for examined in section V. The PSO algorithm has been imple-
this purpose in long transmission lines in modern power mented in the section V to determine optimal location and
systems [2]. In [3] TCSC and SVC were used as re-dispatch parameters of the TCSC controller. The validity of the pro-
tools for congestion management with Total Transfer posed methods have been tested in a 3-machine, 9-bus sys-
Capability (TTC) improvement. tem.
Another challenging problem to the researcher in modern
power industry is the low frequency power oscillations. II. CONGESTION MANAGEMENT
Traditionally [4], Power System Stabilizers (PSS) have been
A. Definition of Congestion Management
Corresponding author: D. Monal, Haldia Institute of Technology, When the producer and consumer of electric energy desire
Dept. Electronics and Instr. Engg., (E-mail: mondald12@yahoo.in)
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2. ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 03, October 2011
to produce and consume an amount of electric energy, the S ij
transmission system may operate at or beyond one or more Tijk  (2)
Pk
transfer limits and the system is said to be congested. In
heavily congested conditions, transmission congestion can The load curtailment at bus k can be obtained as
be relieved by curtailing a portion of non-firm transactions.
An example of two-bus system has been shown in Fig. 1 to Tijk
Pkcut  ΔS ij
explain transmission congestion.
 Tijk (3)
In Fig. 1(a), the maximum real power output of the machine N
is 60 MW, the transmission line flow limit is 55 MW and the
load is 58 MW. Therefore, there is a transmission overload in where S ij  S ij  S ij
the transmission line to serve the load. Congestion can be
alleviated by curtailing some portion of the load. In Fig. 1(b), S ij : Actual power flow through transmission line i-j
the load has been curtailed from 58 MW to 54 MW and the
congestion has been alleviated. S ij : Flow limit of transmission line i-j
B. Methods of Congestion Management N : Total numbers of branches between load buses.
The higher the TLF sensitivity the more is the effect of a
The power flow Pij through the transmission line i-j is a single MW power transfer at any bus. Hence, based on the
TLF sensitivity values the loads are curtailed in required
function of the line reactance X ij , the voltage magnitudes amounts at the load buses in order to eliminate the
Vi , V j and the phase angle ( δ i  δ j ) between the sending transmission congestion on the congested line i-j.
and receiving end voltages (,) as shown in equation (1). III. SMALL SIGNAL MODELING
ViV j A. Modeling of TCSC
Pij  sin (δi  δ j ) (1)
X ij The basic TCSC module and the transfer function model
From equation (1), it has been seen that the power flow can of a TCSC controller [11] have been shown in Fig. 2(a) and
be affected with the change of voltage magnitudes, the 2(b) respectively. This simple model utilizes the concept of a
reactance of the transmission lines or the power angle (). It variable series reactance which can be adjusted through
has been well established that voltage magnitudes can be appropriate variation of the firing angle (  ). The controller
controlled through VAR support. The reactance of the line comprises of a gain block, a signal washout block and a phase
can be reduced through series compensation and the power compensator block. The input signal is the normalized speed
angle can be varied via power injection changes at either deviation ( ν ), and output signal is the stabilizing signal
bus, e.g. generation or load changes. In this paper, series (i.e. deviation in conduction angle, Δσ ).
compensation of line reactance has been considered for Neglecting washout stage, the TCSC controller model can
congestion management and TLF sensitivity (sensitivities be represented by the following state equations;
of the line flows to load change) value for the most overloaded
line has been considered for calculating the necessary load 1 K  1  K T 

     tcsc 
T
ω  tcsc  1


ω (4)
curtailment. T2 ωs  2  ω s  T2



 1 1
X tcsc     X tcsc (5)
Ttcsc Ttcsc
Fig. 1(a) Two-bus system with congestion
Fig. 2(a) TCSC module
Fig. 1(b) Two-bus system with congestion alleviated
The TLF sensitivity at a bus k due to change load power
Pk for a congested line i-j is Tijk and has been calculated
using the relation shown in equation (2) Fig. 2(b) Structure of a TCSC based controller
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3. ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 03, October 2011
The steady-state relationship between the firing angle  Eliminating ΔI g from the respective equations (8)-(11), the
and the equivalent TCSC reactance, X tcsc is given by [12] overall system matrix with TCSC controller can be obtained
as
X tcsc   X C  C1(2(π  α)  sin (2(π  α)))
 C 2 cos 2 (π  α)( ω tan ( ω (π  α))  tan (π  α)) (6) Atcsc ( 7m  2 )  ( 7 m  2 )  A  B D 1 C  (16)
XC X L X C  X LC
where X LC  X  X , C1 
C L π
4X 2
LC
and C 2  . The linearized TCSC equivalent reactance
πX L
can be obtained from (6) is Fig. 3 Block-diagram: Power System with TCSC controller
The block-diagram of the power system with TCSC as a
ΔXTCSC   2C1(1 cos(2α)) C2 sin(2α)( tan((π  α)) tanα) feedback controller has been depicted in Fig. 3.
 cos  (  )  IV. FORMULATION OF OPTIMIZATION PROBLEM
 C     (7)
 
 cos  ( (  ))   A.Particle Swarm Optimization (PSO)
Particle Swarm Optimization was first developed in 1995
B. Multimachine model with TCSC by Eberhart and Kennedy [8]. The PSO algorithm begins by
initializing a random swarm of M particles, each having R
The general theory of the small signal stability problem in
unknown parameters to be optimized. At each iteration, the
case of a multimachine system and its linearized model has
fitness of each particle has been evaluated according to the
been described in [13] and is given by
selected fitness function. The algorithm stores and

ΔX  A1ΔX  B1ΔI g  B 2 ΔV g  E1ΔU (8) progressively replaces the best fit parameters of each particle
( pbesti, i=1, 2, 3, . . . , M) as well as a single most fit particle
0  C 1 Δ X  D 1 Δ I g  D 2 Δ Vg (9) (gbest) among all the particles in the group. The parameters
of each particle (pi) in the swarm are updated in each iteration
0  C 2 ΔX  D3ΔI g  D4 ΔV g  D5 ΔVl (10) (n) according to the following equations:
0  D6 ΔV g  D7 ΔVl (11) veli (n)  w veli (n  1)  acc1  rand1  (gbest  pi (n  1))
The multimachine linearized model with TCSC controller  acc2  rand2  (pbesti  pi (n  1))
can be formulated separately by adding the state variables (17)
Δxtcsc  Δ ΔX tcsc T corresponding to the TCSC (18)
pi (n)  p i (n  1)  veli (n)
controller in equations (8)-(10) and the TCSC power flow
equation in the network equation (11). where, vel i (n) is the velocity vector of particle i and is a vector
The TCSC linearized real power flow equation between of random values  (0,1) . acc1, acc2 are the acceleration
bus s and t can be obtained from the following equation coefficients that pull each particle towards gbest and pbesti
Pst Pst Pst Pst Pst positions respectively and are often set to be 2.0. w is the
ΔPst  Δθ s  ΔV s  Δθt  ΔVt  Δα (12)
θ s V s θt Vt α inertia weight of values  (0,1) . rand1 and rand2 are two
uniformly distributed random numbers in the ranges [0, 1].
where, Pst  V s2 g st  V s Vt (g st cos δ st  b st sin δ st ) (13)
B. Objective Function and Optimization Problem
1 R st  j(X st  X tcsc ) The problem is to search the optimal location and the
Y  
and st R  j(X  X tcsc )
st st R st  (X st  X tcsc ) 2
2 parameter set of the TCSC controller using PSO algorithm.
The objective is to maximize the damping ratio ( ) as much
 g st  jbst (14) as possible. This results in minimization of the critical damping
index (CDI) given by
Ps Pst
with  CDI  J  (1   i ) (19)
α α
Here,  i is the damping ratio of the i-th critical swing mode.
  
 V s2 g  V sVt ( cos δ st g  sin δ st b ) There are four unknown parameters: the TCSC controllers
α st α st α st (15)
gain (Ktcsc), lead time (T1), the lag time (T2) constants and
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4. ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 03, October 2011
TCSC location number (Nloc). These parameters have to be V. APPLICATION AND RESULTS
optimized by minimizing the objective function J given by
the equation (19). With a change of location and the A. Application of TLF sensitivity on Load Curtailment
parameters of the TCSC controller, the damping ratio ( ) as This section describes first the transaction curtailment
well as J varies. result using TLF sensitivity procedures, and then the results
The optimization problem can then be formulated as: obtained along with the application of TCSC in the proposed
Minimize J [Equation (19)] system. Suppose maximum output of all the generators are
S. T. : 250 MW. The real load increased at bus 5 is 50 % more than
Equality Constraints the base load.
n
PGi  PLi  V V Y
k 1
i k ik ( X tcsc ) cos(θi θ k   ik )  0 (20)
n
QGi  QLi  V V Y
k 1
i k ik ( X tcsc ) sin(θi θ k   ik )  0 (21)
Inequality constraints
Vi  1  0.05
min max min max
PGi  PG  PGi ; QGi  QG  QGi
min max
K tcsc  K tcsc  K tcsc ; T1min  T1  T1max
T2min  T2  T2max ; min max
N loc  N loc  N loc
C. Particle configuration
The particle can be defined as a vector which contains
the TCSC controller parameters and the location number: Ktcsc,
T1, T2 and Nloc as shown in equation (22)
Particle: [Ktcsc T1 T2 Nloc ] (22)
The particle configurations corresponding to the TCSC
controller has been shown in Fig 4. The initial population has
been generated randomly for each particle and are kept within Fig. 5 3-machine, 9-bus system with the application of TCSC
a typical range.
It is assumed that the flow limits of lines # 4, 5 and 9 as 70
To optimize equation (19), routines from PSO toolbox [14]
MW and for the lines # 6, #7 and 8 as 100 MW, execution of
have been used. The objective function corresponding to
power flow calculation (Table I) revealed that the line # 4 is
each particle has been evaluated by small signal analysis
congested as the real power flow through this line is 85.44
program of the proposed test system (Fig. 5). This system
MW which has exceeded its proposed flow limit of 70MW.
has been widely used in literature [13] for small signal stability
Therefore, some transaction must be curtailed to avoid
analysis in power systems. The network branches (line # 4, 5,
congestion in order to maintain the safety of the system.
6, 7, 8 and 9) between two load buses are selected here for
Applying TLF sensitivity procedure, the required amount of
locating the TCSC module and therefore, line #4 and line #9
load curtailment is obtained as 17.12 MW (Appendix A.2).
min max After installation of TCSC it has been found that the
are given as N loc and N loc respectively.
.
amount of load curtailment is significantly reduced and the
effect is different for different locations of the TCSC module
(Table II). In this stage therefore, it may be essential to choose
a suitable installing location of the TCSC controller. The
optimal location of TCSC can be determined based on different
factors such as the stability of the system, the load pattern of
the system, the type of contingencies etc. In the following
section the issue of small signal stability problem has been
considered to identify the optimal location of the TCSC
Fig. 4 Particle configuration for TCSC controller controller.
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5. ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 03, October 2011
TABLE I. POWER FLOW AFTER LOAD INCREASE WITHOUT TCSC TABLE III. EIGENVALUES WITHOUT AND WITH TCSC
TABLE II. POWER FLOW AFTER INSTALLATION OF TCSC TABLE IV. PSO BASED TCSC PARAMETERS AND LOCATION
The results obtained in Table II indicate that when TCSC has
been installed in line # 6 power flow through the congested
line # 4 is well below its flow limit (70 MW) and the required
amount of load curtailment in the over loaded bus 5 is zero.
This implies that installation of TCSC in line # 6 alleviated
power flow congestion 100 % in the congested line # 4 without
curtailment of any load.
B. Application of PSO and Small Signal Stability Analysis
In this section the validity of the proposed PSO algorithm
has been tested on a 3-machine 9-bus system (Fig. 5). The
complex eigenvalues of the system with and without TCSC
dynamics are listed in Table III. It has been observed that
without TCSC dynamics the electromechanical mode #1 is
the critical one as the damping ratio of this mode is smallest
compared to other modes. Therefore, stabilization of this mode
is essential in order to improve small signal stability. The
PSO algorithm (Fig. 6) generates the optimal location and the
optimal values of the TCSC controller parameters
simultaneously by minimizing the desired objective function
J and the results are represented in Table IV. The convergence
rate of objective function J for gbest with the number of
generations for 200 has been shown in Fig. 7.
Fig. 6 Algorithm of the implemented PSO
It is evident form Table IV that the application of PSO
based TCSC controller in its optimal location (line # 6)
improved the damping ratio of the critical swing mode # 1
about 33% over the damping ratio without TCSC controller.
In view of the results obtained in Table II and Table IV it may
be reasonable to conclude that the line #6 is the best location
of the TCSC controller.
Fig. 7 Convergence of the objective function for gbest
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6. ACEEE Int. J. on Control System and Instrumentation, Vol. 02, No. 03, October 2011
VI. CONCLUSIONS 45.76
Tijk  = 0.7321;
The investigation in this paper illustrated two novel uses 62.5
of TCSC in a multimachine power system. The TCSC has S ij  (78.11 -70.0)=8.11 MW
been installed for both dynamic and steady state secure
operation of the power system. The optimal location of the The amount of load curtailment at bus 5 is now
TCSC has been determined based on small signal stability 0.7321
improvement in order to achieve dynamic stability. TLF Pkcut   8.11 =12.73 MW
0.4662
sensitivity based load curtailment method has been used
also to constrain line flow congestion. A new stochastic Improvement of load curtailment
optimization technique, PSO algorithm, has been implemented 17.12  12.73
=  25.64%
for optimal parameter setting and identification of the optimal 17.12
site of TCSC controller by minimizing the desired objective
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