A Review on Power System Voltage Stability and Optimization Techniques

T. Madhuranthaka. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 6, Issue 11, ( Part -4) November 2016, pp.06-14
www.ijera.com 6 | P a g e
A Review on Power System Voltage Stability and Optimization
Techniques
T. Madhuranthaka *, Dr.T.Gowri Manohar **
*( Research Scholar, Department of Electrical Engineering, SVU College of Engineering, Tirupathi-517501
** (Professor, Department of Electrical Engineering, SVU College of Engineering, Tirupathi-517501
ABSTRACT
Power system voltage stability is a one of the major focused areas in recent days due to mismatch between
generation and demand. Maintenance of voltage stability is a challenging issue in planning and security
assessment of power systems. Voltage stability is the ability of a power system to maintain steady acceptable
voltages at all buses in the power system under normal operating conditions and after being subjected to a
disturbance. Long-term voltage instability problems can occur in heavily loaded systems where the electrical
distance is large between the generator and the load. Timely application of reactive power compensation or load
shedding may prevent this type of voltage instability. System reactive power handling capacity can be improved
with Flexible AC Transmission System (FACTS) devices. Identification of critical system locations to
undertake appropriate remedial measures in operation is the concern. This paper reviews the performance of
various types FACTS controllers in power system voltage stability problem and focuses on different
optimization methods implemented for optimal placement and sizing of FACTS devices to minimize power
losses.
Keywords: Power System Voltage Stability, Flexible AC transmission System (FACTS), FACTS Controllers,
Optimization algorithms, Fault location identification.
I. INTRODUCTION
Historically the synchronous stability, low
frequency oscillations (1-3 Hz) and sub-synchronous
resonance phenomenon were the major challenges
for both the planners and operators of power
systems. The evolution of modern power systems
with high levels of reactive power compensation and
increased levels of system stress due to mismatch of
power generation and load demand and continuous
transmission system expansion have placed voltage
stability as one of the primary concerns in system
planning and operation. Voltage collapse
phenomenon occurred in power systems of several
developed as well as developing countries from the
seventies onwards and attracted the attention of
researchers in this area. Efficient algorithms for
voltage stability assessment and enhancement are
essential to ensure reliable system operation [1- 3]
Voltage stability like any other stability is a
dynamic problem. However, from static analysis
certain useful information such as loadability limit
and proximity of the operating point to this limit can
be obtained. The pattern of the voltage decay in the
vicinity of voltage collapse point will not be known
from static analysis. Dynamic analysis on the other
hand can be used to study as to how the system
proceeds towards voltage instability and collapse. In
dynamic analysis, dynamic models of system
components that influence voltage stability are
included. The differential and algebraic equations
characterizing the system dynamics are solved in
order to assess the dynamic voltage stability [4-5].
The dynamic analysis can be either for a
small-scale disturbance or for a large-scale
disturbance. The small disturbance voltage stability
is concerned with the voltage stability of an
equilibrium point for small perturbations. The large
disturbance voltage stability is concerned with the
transition to and existence of a new stable state
following a large disturbance like a sudden load
increase, loss of a heavily loaded line, a major
generator/transformer outage etc. An important
aspect in both small-scale and large-scale
disturbance voltage stability analyses is the proper
representation of the load characteristics [6].
The P-V (Q-V) curves have been used to
analyze voltage stability of a power system. P-V
curves are useful to calculate the amount of
corrective measure needed to achieve a desired MW
margin. For constant current and constant
admittance loads the system is voltage stable
throughout the P-V curve. For constant power load,
voltage stability limit and maximum loadability are
the same [7].
Several indices have been evolved to find
the nearness of the operating point to nose point.
Most of the static conditions derived in the literature
to predict voltage instability problem are related to
singularity of the power flow Jacobian. The
singularity of the Jacobian is necessary but not
sufficient to indicate voltage instability (Sauer and
RESEARCH ARTICLE OPEN ACCESS

Pai, 1990) [8]. The common drawback of most of
the indices such as minimum singular value, L-index
etc., is that they are non-linear in nature. One cannot
prescribe a threshold value of the above indices for
taking timely corrective control action. Dynamic
analysis generally requires simulation with dynamic
representation of power system components. The
load modeling is very important in dynamic analysis.
In the literature, it is suggested that static load model
can be used for power flow and also for dynamic
simulation at locations that are far away from the
disturbance. Induction motor models are
recommended at locations that are closer to the
disturbance. The voltage instability phenomenon
caused by induction motor loads is fast. Therefore,
to prevent such phenomenon, fast acting Var
Compensators such as Static Var Compensator
(SVC) (Hammad and El-Sadek, 1989) [9] &
STATCOM, UPFC are being used.
A. Principal Causes of Voltage Stability Problems
Some of the main causes for occurrence of
voltage instability are due to unsuitable locations of
FACTS controllers, high reactive power
consumption at heavy Loads, occurrence of
contingencies, reverse operation of ON Load Tap-
Changer (OLTC), voltage sources too far from load
centers, poor coordination between multiple FACTS
controllers, presence of constant power loads,
difference in transmission of reactive power under
heavy loads [1].
B. Prevention of Voltage Nstability
Prevention of voltage instability can be
accomplished by various methods. Few of them are
i. by placement of Series or Shunt Capacitors, ii. by
placement of FACTS Controllers. iii. Coordination
of Multiple FACTS Controllers. iv. Generation
rescheduling. v. Under-Voltage Load Shedding.
vi. Blocking of Tap-Changer under Reverse
Operation. vii. Installation of Synchronous
Condensers [1].
II. FLEXIBLE AC TRANSMISSION
SYSTEM (FACTS) DEVICES
FACTS devices are used for control of
transmission voltage and power flow, reducing
reactive losses and damping of power system
oscillations for high power transfer levels. FACTS
controllers are used in power systems to increase the
transmission capacity of the line and to provide the
optimum utilization of the existing power systems
controllability.
FACTS devices are cost effective
alternatives to new transmission line construction.
The concept of FACTS embraces a wide variety of
tasks pertaining to both networks and consumers
problems, especially related to power quality issues.
FACTS devices can be categorized into series and
shunt devices.
A. Shunt Facts Devices
The shunt devices are primarily used for
reactive power compensation and therefore voltage
control. The series devices are compensating
reactive power. With their influence on the effective
impedance on the line they have an influence on
stability and power flow. These devices are installed
on platforms in series to the line. Most
manufacturers count Series Compensation, which is
usually used in a fixed configuration, as a FACTS-
device. The reason is that most parts and the system
setup require the same knowledge as for the other
FACTS-devices.
More and more growing importance are
getting the FACTS-devices in shunt and series
configuration. These devices are used for power
flow controllability. The higher volatility of power
flows due to the energy market activities requires a
more flexible usage of the transmission capacity.
Power flow control devices shift power flows from
overloaded parts of the power system to areas with
free transmission capability [3].
i. Static Var Compensator
SVC is a shunt connected FACTS device,
which can be used in Voltage control mode or Var
control mode. Here the term static is used to indicate
that SVC has no rotating part unlike synchronous
machine. SVC is used in voltage control mode by
controlling the reactive var in the system where it is
connected. SVC can draw leading or lagging var to
control the voltage fluctuation or voltage regulation
in the system. If there is a dip in the voltage then it
supplies reactive power and if there is a rise in the
voltage then it absorbs reactive power. So the SVC
can be used as a source or sink of reactive Var in
accordance to the need of system. [10].
Fig. 1: Structure of SVC
ii. Static Synchronous Compensator
STATCOM is widely used dynamic shunt
compensator for controlling reactive power in
transmission and distribution. STATCOM has a
small size, a faster speed, a wide operation range has
a great advantage in performance and can effectively
compensate the reactive power, suppress harmonic

current and provide voltage support for transmission
system, it will be more and more widely used [11].
Fig. 2: Structure of STATCOM
B. Series Facts Devices
Series capacitive compensation in AC
transmission systems can yield several benefits, such
as increased power transfer capability and enhanced
transient stability [12]. Series devices are developed
from fixed or mechanically switched compensations
to the Thyristor Controlled Series Compensation
(TCSC) or even Voltage Source Converter based
devices. Reactive power compensation is provided
to minimize power transmission losses, to maintain
power transmission capability and to maintain the
supply voltage. Series compensation controls the
line impedance of a transmission line; namely the
change of impedance by either inductive or
capacitive compensation [3].
i. Thyristor Controlled Series Capacitor (TCSC)
TCSC controllers use thyristor-controlled
reactor (TCR) in parallel with capacitor segments of
series capacitor bank. The combination of TCR and
capacitor allow the capacitive reactance to be
smoothly controlled over a wide range and switched
upon command to a condition where the bi-
directional thyristor pairs conduct continuously and
insert an inductive reactance into the line. TCSC is
an effective and economical means of solving
problems of transient stability, dynamic stability,
steady state stability and voltage stability in long
transmission lines [13].
Fig. 3: Structure of TCSC
ii. Static Synchronous Series Compensator
SSSC is a series FACT device. The
operating principle is similar to STATCOM but
more complicated as it requires platform mounting
and protection. [1-3]. SSSC consists of VSC and DC
link capacitor and a coupling transformer. SSSC is
able to generate a compensating voltage and is
controllable over the capacitive or inductive range.
Also the SSSC has the ability of interfacing with
external DC power supply to provide compensation.
This compensation can be achieved by injecting the
phase voltage into the transmission line through the
coupling transformer, so the active power flow can
be directly controlled. SSSC injects voltage in series
with the transmission line at 90° to line current. As
per requirement, voltage can be injected in
quadrature with line current in inductive or
capacitive mode [14].
Fig. 4: Structure of SSSC
C. Combined Shunt and Series Facts Devices
i. Unified Power Flow Controller
The Unified Power Flow Controller
(UPFC) is a novel power transmission controller.
The UPFC provides a full dynamic control of
transmission parameters, voltage, line impedance
and phase angle [15]. The UPFC consists of a series
device acts as a controllable voltage source Vc,
whereas the shunt device acts as a controllable
current source IC. The main purpose of the shunt
device is to regulate the dc link voltage by adjusting
the amount of active power drawn from the
transmission line. In addition, the shunt device has
the capability of controlling reactive power.
Fig. 6: Unified power Flow Controller
ii. Dynamic Power Flow Controller (DPFC)
The DPFC consists of one shunt and
several series-connected converters. The shunt
converter is similar as a STATCOM, while the series

converter employs the D-FACTS concept, which is
to use multiple single-phase converters instead of
one large rated converter. Each converter within the
DPFC is independent and has its own dc capacitor to
provide the required dc voltage [16].
.
Fig. 7: Dynamic Power Flow Controller
III. POWER SYSTEM
OPTIMIZATION TECHNIQUES
The selection of location to place the
FACTS device on the transmission line plays a vital
role in maintaining the power system stability.
Various optimization techniques have been proposed
and implemented in the last few decades. Recent
advances in computer engineering and the
complexity associated with power system
optimization have resulted in the need to develop
programming techniques that can be used to find the
optimal location for placing the FACTS devices.
These include dynamic programming, Lagrange
multiplier methods, heuristic techniques, and
evolutionary techniques such as genetic algorithms.
These techniques are often hybridized with many
other intelligent system techniques, including
artificial neural networks (ANN), expert systems
(ES), tabu search algorithms (TS), and fuzzy logic
(FL) [17].
A brief survey on optimization techniques
used in power system are discussed further.
In 2002, A. G. Bakirtzis, P. N. Biskas, C. E.
Zoumas, and V. Petridis published a paper on
“Optimal power flow by enhanced genetic
algorithm”, IEEE Transactions on Power Systems,
vol. 17, no. 2. It presents an Enhanced Genetic
Algorithm for the solution of optimal power flow
with both continuous and discrete control variables.
The advantage of using this method is its modeling
flexibility.
In 2003, S.Gerbex, et.al presented a paper
“Optimal location of FACTS devices to enhance
power system security” in IEEE Bologna Power
Tech Conference in Bologna, Italy from 23-26 June.
In this paper, three heuristic methods Simulated
Annealing (SA), Tabu Search (TS) and Genetic
Algorithms (GA) are compared by using the
parameters: location of devices, their types and
sizes. Five types of FACTS (TCSC, TCVR, TCPST,
SVC and UPFC) devices were modeled. Power
system security was employed as a measure of
performance It concluded that although the three
methods lead to similar results, Tabu Search and
Genetic Algorithms converges faster than Simulated
Annealing [19].
In 2005, Weerakorn Ongsakul, et.al
“Optimal Allocation of FACTS Devices to
Enhance Total Transfer Capability Using
Evolutionary Programming” in IEEE
Transactions, an evolutionary programming (EP) is
proposed to determine the optimal allocation of
FACTS devices for maximizing the total transfer
capability (TTC) of power transactions between
source and sink areas in deregulated power system.
Results from the test system indicate that optimally
placed OPF with FACTS devices could enhance the
TTC value far more than OPF without the FACTS
devices [20].
In 2006, Sidhartha Panda, et.al proposed a
paper “Improving power system transient
stability with an off–centre location of shunt
FACTS devices” in Journal of Electrical
Engineering. It describes that when shunt FACTS
(SVC and STATCOM) devices are placed at the
center of the transmission line, they controls the
power flow and if they locate the FACTS devices at
the off-centre of transmission line, it increases
system stability [21]. The paper concluded that when
FACTS devices are placed at off centre position they
can give better performance and best results to
maintain power system stability.
In 2007, J. Nikoukar, M. Jazaeri presented
a paper “Genetic Algorithm Applied to Optimal
Location of FACTS Devices in a Power System”
in Proc. of the 3rd IASME/WSEAS Int. Conf. on
Energy, Environment, Ecosystems and Sustainable
Development in Agios Nikolaos, Greece. This paper
proposed a genetic algorithm to determine the
location of multiple FACTS devices in a power
system. The simulation performed on IEEE 30 bus
system results proved that the method is efficient
and also optimizes the location, type and rated value
of the FACTS device [22].
In 2008, K.Vijayakumar, R.P
Kumudinidevi published a paper “A hybrid genetic
algorithm for optimal power flow incorporating
FACTS devices” in Asian Journal of Scientific
Research. In this paper, a Genetic algorithm is
proposed which simultaneously can find optimal
generation, optimal choice, and location of FACTS
devices subjected to equality and inequality
constraints [23].
In 2009, S. Auchariyamet, S.
Sirisumrannukul published a paper on “Optimal
Reactive Power Planning with FACTS devices by
Particle Swarm Technique”. This paper explained
a particle swarm optimization based technique to
determine the optimal location of SVC and TCSC
for reactive power planning [24].

In 2010, a paper “Optimal Allocation Of
FACTS Devices In Deregulated Electricity
Market Using Bees Algorithm” of R.Mohamad
Idris, et.al was published in WSEAS Transactions
On Power Systems. Here a traditional method BA
was taken to optimize the parameters of FACTS
devices to increase available transfer capacity in a
power system [25]. The parameters were: location,
types and sizes. The FACTS devices used are TCSC,
SVC, UPFC and TCPST. Simulations were
performed in IEEE 9 and 118 bus system. The
method was also compared with GA for the results.
They concluded that BA is best to optimize the
FACTS devices.
In 2011, F.S. AL-Ismail, M.A. Abido
published the paper “The impact of statcom based
stabilizers on power system Stability, using
intelligent computational optimization
Approach”. The paper explains the application of
STATCOM based stabilizers for power system
stability. The Stabilizer tuning parameters are
optimized by using Tabu Search, Genetic Algoritm,
Simulated Annealing, Differential Evolution,
Evolutionary programming and particle swarm
optimization. The DE as well as PSO based
stabilizer shows an enormous improvement in terms
of overshoot magnitude and settling time as well as
the stability of the power system is increased [26].
In 2012, V.Ravi Dr.K.Duraiswamy has
presented a paper “Effective Optimization
Technique for Power System Stabilization using
Artificial Bee Colony” in the 2012 International
conference on computer communication and
informatics- IEEE at Coimbatore, India. They
explained application of Artificial Bee Colony
Algorithm for power system optimization. The
objectives of the multi-machine system shows better
convergence with ABC technique [27].
Whei-Min Lin, Kai-Hung Lu, et.al
presented their paper “Optimal Location Of
FACTS For Voltage Stability Using Modified
Particle Swarm Optimization” in Proceedings of
The International Multi Conference of Engineers
And Computer Scientists, 14-16 March in Hong
Kong. In this paper, MPSO technique was proposed
to optimize the location of UPFC with ECI model to
increase voltage stability [28]. Simulation was done
in IEEE 30 bus system. They concluded that the
MPSO gave best performance to optimize the UPFC
to improve voltage stability. Simulation presented
that the technique achieved best results after
optimization.
Mahdi M. M. El-arini, Raef S. S. Ahmed,
published a paper on “Optimal Location of Facts
Devices to Improve Power Systems
Performance”. The paper describes a novel
algorithm for finding the optimal location of FACTS
devices based Elitist Non Dominated Sorting
Genetic Algorithm (NSGA-II). IEEE-14 bus is used
to test the performance of the method. The results
proved that NSGA-II performed well when the
optimal power flow and optimal location parameters
are considered [29].
In 2013, Tissa Tom and Rinku Scaria in
their paper “Active and Reactive Power
Compensation in Distribution System Based on
Biogeography Based Optimization Technique”
had proposed a Bigeography Based Optimization
technique for active and reactive power
compensation of distributed systems. It is tested on
the IEEE 13 bus system. It is proved that the active
power and reactive power loss are reduced by a
considerable percentage [30].
In the paper “Power System Stability by
Reducing Power Losses using Optimization
Techniques”, S.N. Deepa and J. Rizwana had
compared two optimization techniques Particle
Swarm Optimization and Modified Particle Swarm
Optimization (MPSO) to reduce the active and
reactive power losses by testing them on IEEE 3-bus
and 6-bus systems. The test results proved that the
performance of MPSO is superior to PSO in
optimizing the power system [31].
Dan Cristian, Constantin Barbulescu,
Stefan Kilyeni, Vasile Popescu, in their paper
“Particle Swarm Optimization Techniques-Power
Systems Applications” has suggested that compared
to other evolutionary techniques, Particle Swarm
Optimization has better global search performance
and faster converges faster[32].
Rekha R & Kannan G in their paper “A
Comparative Analysis on Reactive Power
Optimization Using Various Techniques in
Deregulated Power System” had proposed a
mathematical model of reactive power optimization
using PSO and GA. Gauss -Seidel method is used in
conjunction with PSO to obtain the optimal power
value. Finally its proved that PSO has higher
performance than Genetic Algorithm [33].
In 2014, the paper “A Hybrid Meta-
Heuristics Optimization Technique for Loss
Minimization and Cluster Identification in Power
System Network” published by S. A. Jumaat ,
I.Musirin, M. M. Othman and H. Mokhlis introduced
a new approach for optimizing the transmission loss,
improving the voltage and monitoring the cost of
installation. The meta-heuristic technique
“Evolutionary Particle Swarm Optimization” is
feasible with PSO and Evolutionary Programming
for cluster development when tested on IEEE 30 bus
system [34].
Chichebe. M. Akachukwu, Abiodun M.
Aibinu, Mark N. Nwohu and 4Habeeb Bello Salau,
in their paper “A Decade Survey of Engineering
Applications of Genetic Algorithm in Power
System Optimization” presented a survey on the

Genetic Algorithm techniques used for power
system optimization. It presented a decade review on
various applications of Genetic algorithms for power
system optimization [35].
In the paper “Optimal Capacitor
Placement and Sizing for Enhancement of
Distribution System Reliability and Power
Quality Using PSO”, Pravin Machhindra Sonwane,
Bansidhar Eknath Kushare presented a technique for
optimal capacitor placement to enhance the
reliability and its sizing using PSO. The optimal
capacitor placement improves the power factor,
reduces the power losses and helps in maintaining
the voltage profile [36].
In 2015, S. Mandal, K. K. Mandal, B. Tudu
published a paper on “ A New Self Adaptive
Particle Swarm Optimization Technique For
Optimal Design of a Hybrid Power System”. This
paper implemented a improved particle swarm
optimization technique for optimal design of PV-
wind battery with diesel generator backup hybrid
system [37].
In the paper “Optimal Placement of
Interline Power Flow Controller (IPFC) to
enhance Voltage Stability” published by B.V.Rami
Reddy, Y.V.Siva Reddy , P.Sujatha, a combination
of fuzzy logic and genetic algorithm is proposed.
The genetic algorithm is used to find the optimal
location to place the Interline Power Flow Controller
where as the fuzzy system is used to determine the
injected capacity of IPFC. The Power flow analysis
is later carried out by the Newton Raphson Method.
The performance of the proposed method is tested
on a IEEE 30 bus system [38].
In 2016, J.STEFFY AMIRTHAM,
Mrs.V.UMA in their paper “Optimal Location of
Unified Power Flow Controller Enhancing
System Security”, had proposed a method to find
the optimal location for UPFC based on real power
performance index in the power system network.
The UPFC is modeled by power injection model.
PSO is used to solve the Optimal Power Flow
problem. [39].
Indrajit N. Trivedi, Pradeep Jangir,
Narottam Jangir, Siddharth A. Parmar, Motilal
Bhoye Arvind Kumar, in the paper “Voltage
Stability Enhancement and Voltage Deviation
Minimization Using Multi-Verse Optimizer
Algorithm”, proposed a meta-heuristic technique
Multi-Verse Optimizer (MVO). IEEE-30 bus
system is used for testing. MVO is compared with
Flower Pollination Algorithm (FPA) and Particle
Swarm Optimization. It is observed that MVO gives
better optimization values compared with the other
two. Also MVO has faster convergence [40].
Himmat Singh, Laxmi Srivastava, in their
paper “Optimal VAR control for real power loss
minimization and voltage stability improvement
using Hybrid Multi-Swarm PSO”, presented a
Hybrid Multi-Swarm particle Swarm Optimization
(HMPSO) algorithm for a multi-objective optimal
reactive power control for minimizing the real power
loss and improving the voltage stability. The results
are obtained by testing on a IEEE 30 bus system.
The results are compared with those obtained using
classical DE, classical PSO and modified DE
algorithms. HMPSO has superior performance and
efficiency compared to the other three [41].
The paper “Multi Objective Optimization
for Optimal Power Flow with IPFC using PSO”
presented by J.Praveen and Dr. B.Srinivasa Rao
proposed the usage of Imterline Power Flow
Controller for maintaining the power system
stability. The Particle Swarm Optimization
technique is used to identify the optimal location for
placing the IPFC. The objectives considered are
generation cost, transmission losses and L-index
[42].
Indrajit N. Trivedi, Pradeep Jangir,
Narottam Jangir, Siddharth A. Parmar, Motilal
Bhoye, Arvind Kumar, in their paper “Voltage
Stability Enhancement and Voltage Deviation
Minimization Using BAT optimization
Algorithm”, explained the application of a novel
meta-heuristic BAT Optimization Algorithm (BOA).
IEEE 30 bus system is used for testing. BOA
provides better optimization compared to Flower
Pollination Algorithm and Particle Swarm
Optimization. The parameters considered are Fuel
cost reduction, voltage deviation minimization and
voltage stability improvement [43].
IV. CONCLUSIONS
The operation of various FACTS devices is
explained in this paper. The application of suitable
FACTS controller enhances the power system
stability. A detailed study of the optimization
techniques is carried out. The current trend is
mostly on the usage of meta-heuristic genetic
algorithms to find the optimal location for placing
the FACTS device.
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