1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
96
AN ENHANCED GENETIC ALGORITHM APPROACH FOR OPTIMAL
PLACEMENT OF FACTS DEVICES TO ENHANCE ATC
Prof. P. Ramanjaneyulu1
, Prof. Dr. V.C. Veera Reddy2
1
Professor, Electrical and Electronics Engineering, KITS, Ramachandrapuram
2
Professor in Electrical and Electronics Engineering Department, SVUCE, Tirupathi
ABSTRACT
In order to facilitate the electricity market operation and trade in the restructured environment,
ample transmission capability should be provided to satisfy the demand of increasing power
transactions. The conflict of this requirement and the restrictions on the transmission expansion in
the restructured electricity market has motivated the development of methodologies to enhance the
available transfer capability (ATC) of existing transmission grids. The insertion of flexible AC
transmission System (FACTS) devices in electrical systems seems to be a promising strategy to
enhance single area ATC and multi-area ATC. This paper determines optimal location and
controlling parameter of TCSC and SVC to maximize Available Transfer Capability (ATC) and
improve Contingency simultaneously using Genetic Algorithm for this purpose as the optimization
tool. In this paper ATC is defined as varying and objective function of Contingency consists of line
congestion alleviation and bus voltage magnitudes enhancement. The Available Transfer Capability
(ATC) of a transmission network is the unutilized transfer capabilities for the transfer of further
commercial activity, over and above already committed usage. Contingency analysis is performed to
detect and rank the severest one-line fault Contingency in a power system. The obtained results show
that TCSC and SVC simultaneously are very effective Devices on ATC enhancement and
Contingency improvement in a power system.
1. INTRODUCTION
Inter-area power transfer can be increased without system security encroachments [2].
Transmission lines contain several physical limits due to thermal capacity, stability, and voltage [1].
Optimization methods have been widely used in conventional power system to solve numerous
problems such as market clearing mechanism, bidding decision, and ATC computation [7].The
Available Transfer Capability (ATC) denotes the unexploited transfer capabilities of a transmission
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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
97
network for the transfer of power for further commercial activity, in addition to already committed
usage [3]. More precisely, ATC is considered as Total Transfer Capability (TTC) less than
Transmission Reliability Margin (TRM), sum of existing transmission commitments (which includes
retail (customer service) and Capacity Benefit Margin (CBM) and assuming the other components
related to ATC are zero for simplicity [4]. Total transfer capability refers to a gauge of the transfer
capability residual in the physical transmission network for further commercial activity in addition to
previously committed uses [5]. Using FACTS devices, the power system performance and stability
can be improved [8]. Flexible Alternating Current Transmission System (FACTS) is an auspicious
technology, which can boost the transmission capacity of the ac lines and can control the power flow
over a certain transmission lines [9]. Also, FACTS devices are competent in controlling the voltage
magnitude, phase angle, and circuit reactance [6]. The power flow arrangements as well as the
reactive power flow in the transmission lines are controlled by means of FACTS technology, such as
Static Var Compensator (SVC), Static Synchronous Compensator (STATCOM), Static Synchronous
Series Compensator (SSSC), and Unified Power Flow Controller (UPFC) [13]. UPFC is one of the
most adaptable and intricate FACTS devices, combining the features of the STATCOM and SSSC
[10].
2. RELATED WORKS
Some of the recent works related to ATC enhancement with FACTS controllers are discussed
below. Rani et al. [11] have proposed a genetic algorithm based technique to identify the best
location for fixing FACTS. Devices for improving the Available Transfer Capability (ATC) of power
transactions between source and sink areas in the deregulated power system. Here, two types of
FACTS have been simulated: Thyristor Controlled Series Compensator (TCSC) and Unified Power
Flow Controller (UPFC) for improving the ATC of the interconnected power system. A Repeated
Power Flow with FACTS devices including ATC has been employed to compute the best possible
ATC value within real and reactive power generation limits, line thermal limits, and voltage limits.
Venkaiah et al. [12] have proposed a Static Security based ATC computation for real-time
applications by means of three artificial intelligent techniques: Back Propagation. Umapathy et al.
[13] have presented an application of probabilistic distribution based interval arithmetic approach to
compute the ATC in a power network in terms of confidence intervals. The interval arithmetic
approach allows integration of the uncertainty in the input parameters and offers strict bounds for the
solution. Here, the deviation of the real power load has been represented as a Gaussian distribution
function. Moreover, the proposed technique has been tested and validated on IEEE
14 bus test system. An application of complex valued neural network for ATC calculations
with and without contingencies have been introduced by Chary et al. [14]. Here, a 9 bus test system
has been used to evaluate the performance. The objective function is to increase the load on certain
source and sink nodes. Also, the voltage limits of the buses and the line losses have been well
considered in this proposed technique. A unified optimization approach has been proposed by
Jayashree et al. [15] for computing Available Transfer Capability (ATC) and performing Congestion
Management (CM) in a deregulated power system handling both pool and bilateral transactions. Here,
a power injection model has been employed for Unified
Power Flow Controller (UPFC), DC load flow model for power network, and repeated linear
programming method for optimization. The DC model enforces the line operating limits in MW. A
computer package has been developed and the efficacy of the proposed unified technique has been
validated on 4 bus and an IEEE 30 bus systems.

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
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3. MODELING OF TCSC AND SVC
Available transfer capability is used in power system to identify the ability of power flow
between two areas for different system conditions. One of the techniques used to improve the
available transfer capability of the transmission line is connecting FACTS controllers in the system.
The major problem in connecting FACTS controller in the system is identifying the optimal location
for fixing FACTS controllers and also computing the amount of voltage and angle to be injected in
the system .Building new transmission lines to meet the increasing electricity demand is always
limited economically and by environmental constraints and FACTS devices meet these requirements
using the existing transmission systems [12]. Two types of FACTS have been used in this study
namely; Thyristor Control TCSC Series Compensator (TCSC) and Static Var Compensator (SVC)
Modeling of TCSC
Transmission lines are represented by lumped equivalent parameters. The series compensator
TCSC is simply a static capacitor/reactor with impedance jxc [13]. Fig. 1 shows a transmission line
incorporating a TCSC. where Xij is the reactance of the line, Rij is the resistance of the line, Bio and
Bjo are the half-line charging susceptance of the line at bus-i and bus-j. Xnew is the new defined as
varying reactance of the line after placing TCSC between bus i and j [13].
Fig. 1: Equivalent circuit of transmission line after placing TCSC
Modeling of SVC
The SVC is a shunt connected static var generator or absorber. The SVC can be used to
control the reactive compensation of a system. BSVC represents the controllable susceptance of
SVC. It can be operated as inductive or capcitive compensation. In this study, it is modeled as an
ideal reactive power injection at bus i, at where it is connected. Fig. 2 shows the equivalent circuits
of SVC at bus i [14].
Fig. 2: Variable shunt susceptance

4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
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In this paper, the objective function of ATC is defined as λ varying and is considered in
following forms
where: PDi , QDi : are real and reactive load demand at bus I and P0
Di , Q0
Di : are original real and
reactive load demand at bus i. kDi: is constant to specify the rate of changes in load as λ varies. In
this paper the value of kDi has been taken as 1. λ is a scalar parameter representing the increase in
load bus and is defined as ATC.
4. CONSTRUCTION OF GENETIC ALGORITHM
In genetic algorithms individuals are simplified to a chromosome that codes the control
variables of the problem. The strength of an individual is the objective function (fitness) that must be
optimized. A random start function might generate the initial population size. After the start,
successive populations are generated using the GA iteration process, which contains three basic
operators: reproduction, crossover and mutation. Finally, the population stabilizes, because no better
individual can be found. When algorithm converges, and most of the individuals in the population
are almost identical, it represents a sub-optimal solution. A genetic algorithm has three parameters:
the population size, crossover rate and mutation rate. These parameters are important to determine
the performance of the algorithm
A. Presentation of control variables
To apply GA to solve a specific problem, one has to define the solution representation and
the coding of control variables. The optimization problem here is to use Continuation Power Flow
(CPF) to find the Total Transfer Capability for different FACTS devices locations and
compensations. Every individual chromosome should contain
B. Initialization
The initialization procedure will select the initial population within the range of the control
variables with a random number generator. The user can specify the population number in this
procedure.
C. Fitness evaluation
After control variables are coded, the objective function (fitness) will be evaluated. These
values are measures of quality, which is used to compare different solutions. The better solution joins
the new population and the worse one is discarded. The fitness value of an individual will determine
its chance to propagate its features to future generations. Here ATC is used as the fitness in the
genetic algorithm.
D. Reproduction
Reproduction is a process in which individual chromosomes are copied according to their
objective function (fitness), This operation is an artificial version of the Darwinian Process of natural
selection. The first stage of the reproduction process is to select chromosomes for mating. Two
different techniques, roulette wheel selection and stochastic universal sampling are tested here. It is
seen that stochastic universal sampling exhibits better convergence.

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
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E. Crossover
Crossover is one of the main distinguishing features of GAs that make them different from
other algorithms. Its main aim is to recombine blocks on different individual to make a new one.
Convex crossover is used in this work as the following formulation.
F. Mutation
Mutation is used to introduce some sort of artificial diversification in the population to avoid
premature convergence to local optimum. An arithmetic mutation operator that has proved successful
in a number of studies is dynamic or non uniform mutation, which is used in this study.
G. Population replacement
Two population replacement methods, nonoverlapping generations and steady-state
replacement are used in this work. When using non-overlapping generations, a generation was
entirely replaced by its offspring created through selection, crossover and mutation. It is possible for
the offspring to be worse than their parents and some fitter chromosomes may be lost from the
evolutionary process. Steady-state replacement is used to overcome this problem. In this process, a
number of offspring are created and these replace the same number of the least fit individuals in the
population. In this work the steady-state replacement demonstrates better convergence than non-
overlapping generations.
5. CASE STUDIES AND RESULTS
A two are power system model from (17) is used to implement the proposed method. The
PSAT (18) tool box, a matlab based tool for power system analysis is used for analyzing and
calculating the ATC. The optimal location of FACTS devices are also checked using this toolbox.
The model is a 11 bus system, 8 lines with 4 generators, 4 Transformers and 2 Loads.
The simulink model of system used is given in the figure (3)
Figure 3: Simulink model of the 2 Area System

6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
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The below figure gives voltage profile of the above system when no FACTS device is placed
Figure 4: Voltage Magnitude Profile without FACTS
Figure (5) depicts. voltage profile of the above system when SVC is placed.
Figure 5: Voltage magnitude profile with SVC
Figure (6) depicts the voltage profile of the system when TCSC is placed.
Figure 6: Voltage magnitude profile with TCSC

7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME
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The steps involved in calculation of the optimal location using GA approach are mentioned
below. This method is implemented using Matlab version 7.1
Step 1: Read the power system bus and line data using NR repeated power flow. Bus data: Bus no,
Bus type, Voltage mag, Angle deg, etc..
Step 2: Read data for genetic operations.
Step 3: Read no. of control variables i.e. TCSC and SVC locations and XTCSC, QSVC.
Step 4: Read line data=[ ] (for calculating Contingency) and Calculate the function that consists
Contingency as (SV1).
Step 5: Read (for calculating ATC) and calculate the function that consists ATC as (SV2).
Step 6: Calculate the function that consists ATC and Contingency simultataneously using NR
repeated power flow as
Table 1: ATCs and Contingencies without and with FACTS devices
State Location ATC value Contingency
No FACTS - 1.42 319.13
With SVC Bus-8 1.49 318.6
With TCSC Bus-8 1.44 317.6
6. CONCLUSION
ATC enhancement and Contingency improvement are two important issues in power systems.
ATC can be usually limited by heavily loaded circuits and buses with relatively low voltage. It is
well known that FACTS technology can control voltage magnitude, phase angle and circuit reactance.
Using these devices may redistribute the load flow, regulating bus voltages. Therefore, the FACTS
utilization enhances the power system security in contingency. This paper has proposed Genetic
Algorithm to find optimal location and setting of the combined TCSC and SVC for maximizing ATC
and minimizing Contingency of power system. Simulations Test results indicate that optimally
placed TCSC and SVC by GA could increase ATC, reduce Contingency in this system.
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