 Open Access
 Total Downloads : 2031
 Authors : P. Ramesh, K. Padma
 Paper ID : IJERTV1IS7455
 Volume & Issue : Volume 01, Issue 07 (September 2012)
 Published (First Online): 25092012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Optimal Allocation of TCSC Device Based on Particle Swarm Optimization Algorithm
P. Ramesh * and K. Padma**
*P.G. Scholar, **Assistant Professor, Department of Electrical Engineering, A.U.C.E (A),A.U., Visakhapatnam530003
Abstract
This paper presents an application of Particle Swarm Optimization (PSO) to find out the optimal location and the optimal parameter settings of TCSC devices under single contingency to eliminate the bus voltage violations and improvement of power flow in a transmission line. The particle swarm optimization (PSO) technique is used to solve the optimal power flow problem for steadystate studies by maintaining thermal and voltage constraints. The suitability of the proposed approach is examined on the standard IEEE 30bus test system with TCSC FACTS device. Simulation results show that proposed PSO algorithm gives better solution to enhance the system performance with TCSC device compared to without TCSC FACTS device.
Keywords: Newton Raphsons Method, TCSC, FACTS device, Particle Swarm Optimization (PSO) technique, Optimal Power Flow, Cost of Generation.

Introduction
The operation mechanism of power system becomes more and more complicated due to the continuously increasing load demand which leads to an augmented stress of the transmission lines and higher risks for faulted lines. Therefore, power system
can be operated in less secure state following unexpected line congestions and low voltages. Construction of new transmission lines can be one solution for leading more stable and secure operation of power systems. But it becomes a timeconsuming process due to political and environmental reasons. Because of all that, it becomes more important to control the power flow along the transmission lines to meet the needs of power transfer [1][2].
Power flow is a function of transmission line impedance, the magnitude of the sending end and receiving end voltages and the phase angle between voltages. By controlling one or a combination of the power flow arrangements, it is possible to control the active as well as the reactive power flow in the transmission line [3].
A new solution to improve the stability and security of the power system is the Flexible AC Transmission Systems FACTS devices [4] can improve the stability of the power network, reduce the flows of heavily loaded lines by controlling their parameters, and maintain the bus voltages at desired level. Consequently they can improve the power system security in contingency [5].
With FACTS technology [6][7] , such as Thyristor Controlled Series Compensator (TCSC), Static Var
Compensators (SVCs), Static Synchronous Compensators (STATCOMs), Static Synchronous Series Compensators (SSSCs) and Unified Power Flow Controller (UPFC) etc ,bus voltages, line impedances and phase angles in the power system can be regulated rapidly and flexibly. Thus, FACTS can facilitate the power flow control, enhance the power transfer capability, decrease the generation cost, and improve the security and stability of the power system.
Thyristor Controlled Series Compensator (TCSC) is one of the most effective FACTS devices which offer smooth and flexible control of the line impedance with much faster response [8]. TCSC can also enhance the transient stability of power system that means if there is any sudden change of load that occurs in power system it will maintain stability without loss of synchronism by using TCSC FACTS device is connected in series with the transmission line and also increase the transfer capability of the transmission system by reducing the transfer reactance between the buses at which the line is connected. However, to achieve the above mentioned benefits, the TCSC should be properly installed in the network with appropriate parameter settings [9]
Thus in this paper, TCSC FACTS controller is
incorporated to solve an optimization problem with different objectives such as minimization of cost of generation, real power loss, enhancement of voltage profile and voltage angles and Lindex as these are the basis for improved system performance. The particle swarm optimization (PSO) based algorithm is used effectively to solve the optimal power flow problem [10], it present great characteristics and capability of
determining global optima, incorporating a set of constraints including voltage stability and FACTS device. In order to calculate the power losses and check the system operating constraints such as voltage profile, a load flow model is used. An existing NewtonRapson load flow algorithm is introduced [11]. This model is further modified to incorporate TCSC FACTS device into the network and PSO technique is applied to the modified model to enhance the performance of the power system. Thus, effectiveness of the proposed method was tested on standard IEEE 30bus system and comparison was made on the performance of without and with TCSC FACTS device.
The organization of this paper is as follows. Section
2 addresses the Computation of Voltage Stability Index (Lindex), FACTS controller such as TCSC is explained in Section 3, Mathematical formulation of OPF problem is given in section 4, Overview of Particle Swarm Optimization Algorithm is represented in section 5, Overall Computational Procedure is given in the section 6, the simulation results is given in section 7, and Comparison of fuel cost of generation with different OPF models is illustrated in section 8, and finally the conclusion is given in section 9.

Voltage Stability Index (Lindex) Computation
The voltage stability Lindex is a good voltage stability indicator with its value change between zero (no load) and one (voltage collapse) [5][6]. Moreover, it can be used as a quantitative measure to estimate the voltage stability margin against the operating point. For a given system operating condition, using the load
flow (state estimation) results, the voltage stability L – index is computed as [5],
system behavior and enhancement of system reliability. However, their main function is to control power flows.
L j = 1
g
i1
Vi
V
Fji
j
(1)
3.1. Thyristor Control series Compensator (TCSC)
j g 1,…, n
All the terms within the sigma on the RHS of equation (1) are complex quantities. The values of F ji are btained from the network Ybus matrix. For
stability, the index L j must not be violated (maximum limit=1) for any of the nodes j. Hence, the global
One of the important FACTS controllers is the TCSC which allows rapid and continuous changes of the transmission line impedance. TCSC [14][16] controls the active power transmitted by varying the effective line reactance by connecting a variable reactance in series with line and is shown in Figure 1.
TCSC is mainly used for improving the active power
indicator L j
describing the stability of the complete
flow across the transmission line.
subsystem is given by maximum of L j for all j (load
buses). An
L j index value away from 1 and close to
0 indicates an improved system security. The
advantage of this
L j index lies in the simplicity of the
numerical calculation and expressiveness of the results.

FACTS controllers
FACTS controllers are able to change, in a fast and effective way, the network parameters in order to
Figure 1. Circuit diagram of TCSC
The active and reactive power equations at bus k are:
k k kk
achieve better system performance. FACTS controllers such asphase shifter, shunt, or series compensation and
Pk VkVm Bkm sin(k m )
(2)
the most recent developed thyristor controlled based
power electronic controllers, make it possible to
Q V 2 B
VkVm Bkm cos(k m )
(3)
control circuit impedance, voltage angle and power flow for optimal operation performance of power systems, facilitate the development of competitive electric energy markets, stimulate the unbundling the power generation from transmission and mandate open access to transmission services, etc. The benefit brought about by FACTS includes improvement of
For the power equations at bus m, the subscripts k and
m are exchanged in Equations (2) and (3).
In NewtonRaphson solutions these equations are linearized with respect to the series reactance. For the condition, where the series reactance regulates the amount of active power flowing from bus k to bus m at
km
a value P reg , the set of linearized power flow
equations is solved to minimize fuel cost of generation maintaining
P
Pk
Pk
Pk V
k
Pk V
m
Pk X
thermal and voltage constraints can be formulated as
k
k
m
Vk
Vm
X TCSC
TCSC k
follows [17][18]:
P
P P
P V
P V
P X
V
m m m m
k
m m
m
TCSC
m NG
k
Q
m
Q
Vk
Q
Vm
Q
X TCSC
Q
k
Minimize F = ((a P2 b P C )
i Gi i Gi i
(6)
Q
k k k V k V
k X V
k
V k
V m X
TCSC
k
i1
k m k m
TCSC
Vm
m
Q
Qm
Qm
Qm V
V k
Qm V
V m
Qm X
X
TCSC Vm
The minimization problem is subjected to
k m k m
TCSC
X
following equality and inequality constraints
P XTCSC P XTCSC P XTCSC P XTCSC
P XTCSC
TCSC
P XTCSC km km km V km V
k
m
km X
X TCSC
k
km V V
m k m
X TCSC
TCSC
(4)

Equality Constraints
These are the sets of nonlinear power flow
Where
TCSC
P
km
is the active power flow mismatch
equations that govern the power system, i.e.
n
PGi PDi Vi V j Yij cos (ij i j ) 0
(7)
for the series reactance
j 1
n
P
P
TCSC
km
reg km
XTCSC,cal

P
km
QGi

QDi

Vi
j1
V j Yij
sin(ij
i

j
) 0
(8)
X is given as
Where
PGi
and
QGi are the real and reactive
TCSC
power outputs injected at bus i , the load demand at the
XTCSC
X
i TCSC
i1 TCSC
same bus is represented by
PDi and QDi , and elements

X
and is the incremental change in series reactance; and
X
of the bus admittance matrix are represented by
andij .
Yij
P TCSC,cal
is the calculated power.
km
The state variable
X TCSC of the series controller is



Inequality Constraints
These are the set of constraints that represent the
updated at the end of each iterative step according to system operational and security limits like the bounds
X
(i)
on the following:
X
X
(i)
TCSC
(i1)
TCSC

TCSC
X
(i1)
X
TCSC
(5)

Generators real and reactive power outputs
TCSC
Pmin P
Pmax ,i 1,, N
(9)
This changing reactance represents the series reactance regulates the amount of active power flowing
from bus k to bus m at a specified value P reg
Gi
Q
min
Gi
Gi
QGi
Gi
Gi
Qmax ,i 1,, N
(10)
km
i


Mathematical formulation of OPF

Voltage magnitudes at each bus in the network
Problem
Mathematically, the OPF problem with FACTS is
min
V
i
Vi
V max ,i 1,, NL
(11)

Transformer taps setting PSO is basically developed through simulation of bird
T
min
i
Ti
T max ,i 1,, NT
(12)
flocking in twodimension space. The position of each individual (agent) is represented by XY axis position
i

Reactive power injections due to capacitor banks Modification of the agent position is realized by the
Qmin Q
Qmax ,i 1,,CS
(13)
position and velocity information.
Ci Ci Ci

Transmission lines loading
An optimization technique based on the above concept can be described as follows: namely, bird
i
i
S S max ,i 1,, nl
(14)
flocking optimizes a certain objective function. Each agent knows its best value so far (pbest) and its XY

Voltage stability index position. Moreover, each agent knows the best value so
Lji
Ljmax ,i 1,, NL
(15)
far in the group (gbest) among pbests [20]. Each agent tries to modify its position. This modification can be
i

TCSC device constraints: Reactance constraint of TCSC
represented by the concept of velocity. Velocity of each agent can be modified by the following equation:
X min X
X max
(16)
vk 1 wvk c rand * ( pbest sk ) c rand
* (gbest sk )
TCSC
TCSC
TCSC
i i 1 1
i i 2 2 i
The equality constraints are satisfied by running the power flow program. The generator bus real power
generations ( Pgi ), generator terminal voltages ( Vgi ),
P
of
transformer tap settings ( Ti ), the reactive power
Where,
v k : Velocity of agent i at iteration k,
i
w : weighting factor,
(17)
generation of capacitor bank ( QCi ), and
reg km
c1 & c2: cognition and social components,
TCSC are control variables and they are selfrestricted by the representation itself. The active power
generation at the slack bus ( Pgs ), load bus voltages
(VLi ) and reactive power generation ( Qgi ), line flows ( Si ), and voltage stability ( L j )index are state
variables which are restricted through penalty function approach.


Overview of Particle Swarm
rand: random number between 0 and 1
s
i
k : Current position of agent i at iteration k. pbesti : the pbest of agent i.
gbest : gbest of group.
i i i
Using the above equation, a certain velocity which gradually gets close to pbest and gbest can be calculated. The current position (searching point in the solution space) can be modified by the following equation:
Optimization
PSO is one of the optimization techniques and belongs to evolutionary computation techniques [19].
sk 1 sk vk 1
(18)

Overall Computational Procedure for Solving the Problem
The implementation steps of the proposed PSO based algorithm can be written as follows;
Step 1: Input the system data for load flow analysis Step 2: Select FACTS devices and its location in the
sytem
Step 3: At the generation Gen =0; set the simulation parameters of PSO parameters and randomly initialize k individuals within respective limits and save them in the archive.
Step 4: For each individual in the archive, run power flow under the selected network contingency to determine load bus voltages, angles, load bus voltage stability indices, generator reactive power outputs and calculate line power flows.
Step 5: Evaluate the penalty functions
Step 6: Evaluate the objective function values and the corresponding fitness values for each individual.
Step 7: Find the generation local best xlocal and global best xglobal and store them.
Step 8: Increase the generation counter Gen= Gen+1.
Step 9: Apply the PSO operators to generate new k individuals
Step 10: For each new individual in the archive, run power flow to determine load bus voltages, angles, load bus voltage stability indices, generator reactive power outputs and calculate line power flows.
Step 11: Evaluate the penalty functions
Step 12: Evaluate the objective function values and the corresponding fitness values for each new individual.
Step 13: Apply the selection operator of PSO and update the individuals.
Step 14: Update the generation local best xlocal and global best xglobal and store them.
Step 15: If one of stopping criterion have not been met, repeat steps 414. Else go to stop 16
Step 16: Print the results

Simulation Results
The proposed PSO algorithm is employed to solve optimal power flow problem incorporating TCSC FACTS device for enhancement of system Performance is tested on standard IEEE 30bus test system.
The PSO parameters used for the simulation are summarized in Table 1
Table 1. Optimal parameter settings for PSO
Parameter
PSO
Population size
20
Number of iterations
150
Cognitive constant, c1
2
Social constant, c2
2
Inertia weight, W
0.30.95
The network and load data for this system is taken from [21]. To test the ability of the proposed PSO algorithm one objective function is considered that is minimization of cost of generation. In order to show the affect of power flow control capability of the
FACTS device in proposed PSO OPF algorithm, two sub case studies are carried out on the standard IEEE 30bus system.
Case (a):power system normal operation (without FACTS devices installation),
Case (b): One TCSC device is installed at an optimal
line connected between buses 9 and 10 with
Control Variables
Limits(p.u)
Without FACTS
With
FACTS
Min
Max
TCSC
PG1
0.50
2.000
1.7718
1.7748
PG2
0.20
0.800
0.4867
0.4888
PG3 PG4
0.10
0.10
0.350
0.300
0.2109
0.1215
0.2080
0.1183
PG5
0.15
0.500
0.2144
0.2147
PG6
0.12
0.400
0.1200
0.1202
VG1
0.95
1.10
1.0868
1.0854
VG2
0.95
1.10
1.0667
1.0656
VG3
0.95
1.10
1.0405
1.0382
VG4 VG5
0.95
0.95
1.10
1.10
1.0645
1.0348
1.0369
1.0338
VG6
0.95
1.10
1.0425
1.0442
Tap – 1
0.9
1.1
1.0464
1.0114
Tap – 2
0.9
1.1
0.9000
1.0187
Tap – 3
0.9
1.1
0.9568
0.9651
Tap – 4
0.9
1.1
0.9623
0.9831
QC10
0.0
0.10
0.0783
0.0993
QC12 QC15
0.0
0.0
0.10
0.10
0.0000
0.0629
0.0000
0.0264
QC17
0.0
0.10
0.0518
0.0276
QC20
0.0
0.10
0.0785
0.0666
QC21
0.0
0.10
0.0386
0.0693
QC23 QC24
0.0
0.0
0.10
0.10
0.0429
0.0260
0.0337
0.0415
QC29
0.0
0.10
0.0260
0.0336
Cost ($/h)
800.8678
800.5671
Ploss (p.u.)
0.0913
0.0904
Ljmax
0.1381
0.1301
CPU time (s)
45.5510
53.8600
Table 2. Optimal settings of control variables for IEEE 30 bus system.
line real power flow Pji
base case values.
is 1.25 times of
The series reactance ( X TCSC ) ratings of TCSC is
set at 50% of the transmissionline, thus inductive Reactance is 0.055.
The first case is the normal operation of network without using any FACTS device, in second case optimal location of one TCSC device has been considered.
From Table 2. it shows that the details of the control variables and the installation of TCSC in the network gives the best performance of the system compared to the without FACTS device in the network in terms of reduction in cost of generation, power loss reduction, maximum of voltage stability indices. It also gives that PSO algorithm is able to enhance the system performance while maintaining all control variables and reactive power outputs within their limits The convergence characteristic of the cost of generation without and with TCSC (one at a time) at
optimal location is shown in figure 2
Figure 2. Convergence of cost of generation without and with TCSC FACTS device for IEEE 30bus system
The Figures 36 shows the percentage MVA loading of the lines, voltage profiles, voltage angles, and voltage stability indices of buses without
and with TCSC at optimal location
Figure 3. Percentage MVA line loadings of IEEE 30 bus system after optimization without FACTS and with TCSC FACTS device
Figure 4. Voltage profiles of IEEE 30bus system after optimization without and with TCSC FACTS device.
Figure 5. Voltage angles of IEEE 30bus system after optimization without and with TCSC FACTS device.
Figure 6. Voltage stability indices of IEEE 30bus system after optimization without and with TCSC FACTS device

Comparison of fuel cost of generation with different OPF models
The comparison of fuel cost of the proposed method with those of the methods reported in the literature is given in Table 3. It can be seen that PSO algorithm gives less cost of generation compared with the cst of generation obtained with other OPF methods.
Method
Fuel Cost ($/hr)
EP [22]
802.907
TS [23]
802.502
TS/SA [23]
802.788
ITS [24]
804.556
IEP [25]
802.465
SADE_ALM [26]
802.404
OPFPSO [19]
800.410
MDEOPF [27]
802.376
Genetic Algorithm ($/hr)
[28]803.050
Gradient method [29]
802.430
PSO (proposed) without
FACTS
800.8678
PSO (proposed) with
TCSC
800.5600
Table 3. Comparison of fuel costs for IEEE 30bus System
(TCSC) scheme is very effective compared to without FACTS in improving the security of the power system.

TCSC FACTS device can increases the power transfer capability of the transmission line by increasing the voltage at their terminals of both ends of transmission line and also decrease the line reactance for improving power transfer capability.

Proper selection of FACTS device and its location can effectively improve the overall system performance.
From Table 3 It can be seen that the PSO algorithm with TCSC gives less cost of generation compared with the cost of generation obtained with other OPF methods


Conclusions
This paper has presented an OPF model incorporating FACTS controller such TCSC using the PSO algorithm for enhancement of system performance. This model is able to solve power networks of any size and converges with minimum number of iterations and independent of initial conditions. The standard IEEE 30bus system has been used to demonstrate the proposed method over a wide range of power flow variations in the transmission system. The simulation results shows that proposed OPF with thyristor Controlled Series Compensator

Finally, the optimization problem with different objectives such as minimization of cost of generation, reduction in power loss, enhancement of voltage profile and voltage angle at every bus and Lindex are improved system performance

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