International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 1- July 2015
Stability Improvement of Power System by Using Fuzzy
Coordinated Static Var Compensator
Ashish Kumar Choubey, Associate Prof.Arti Bhandakkar
Student M.tech Power System, Associate Professor, Department of Electrical Engineering,
Shri Ram Institute of Technology Jabalpur MP, INDIA
Abstract: In emerging power systems, enlarged
communication often lead to the situations where the
structure no longer remains in secure operating region.
The flexible Ac transmission system (FACTS) controllers
can take part in an important role in the power system
security enhancement. However, due to high capital
investment, it is necessary to locate these controllers
optimally in the power system. Static Var Compensator
(SVC) is a shunt type FACTS device which is used in
power system primarily for the purpose of voltage and
reactive power control. In this paper, a fuzzy coordinated
supplementary controller Static Var Compensator (SVC)
is developed.
The static var compensators SVC are FACTS
devices in shunt connection which can be used for power
system enhancement. The paper investigates a modern
approach for SVC control using fuzzy logic based
controller. The simulations and effects of shunt
compensation on power system transmission stability are
also presented. The performances of fuzzy based control
of the SVC are compared with a conventional
compensation and the advantages of modern control to
offer significant damping to the system oscillations are
highlighted. Matlab Simulink environment was used for
system modeling and simulations.
Keywords: FACTS, Fuzzy logic controllers, Stability,
SVC.
I. INTRODUCTION
Today Transmission & Distribution network of
power systems are very stressed due to growing
demand of better quality of power at lower cost. As
a result transmission networks are operating on
high transmission levels. Transient stability,
damping oscillations etc are the major operating
problems that power engineers are confronting
during transmitting power at high levels. Transient
Stability indicates the capability of the power
system to maintain synchronism when subjected to
severe transient disturbances such as fault on
heavily loaded lines, loss of a large load etc.
Generator excitation controller with only excitation
control can improve transient stability for minor
faults but it is not sufficient to maintain stability of
system for large faults occur near to generator
terminals .Researchers worked on other solution
and found that Flexible ac transmission systems
(FACTS) is one of the most prominent solution that
can improve stability by changing electrical
characteristics of Power system. [1]
Under dynamic conditions such as faults, line
openings, generator tripping and load throw off,
etc. protective systems are designed with more
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emphasis on protecting the equipments than
concern to the system security and stability.
However, judicious use of dynamic controls at
generating systems, excitation/governor systems,
HVDC systems, static compensators and more
recently FACTS devices will help to maintain the
system security/stability. In a day-to-day operation
it may be beyond the operator’s scope to take any
control decision during emergencies and use
various control devices. The first justification is
correct, but does not characterize the unique nature
of fuzzy systems theory. In fact, almost all theories
in engineering characterize the real world in an
approximate manner. For example, most real
systems are non linear, but we put a great deal of
effort in the study of linear system. A good
engineering theory should be precise to the extent
that it characterizes the key features of the real
world and, at the same time, it is tractable for
mathematical analysis. In aspect, fuzzy systems
theory does not differ from other engineering
practices. [2]
Flexible AC Transmission Systems (FACTS)
devices with a suitable control strategy have the
potential to increase the system stability. Shunt
FACTS devices play an important role in reactive
power flow in the power network. In large power
systems
low
frequency
electro-mechanical
oscillations often follow the electrical disturbances.
Therefore SVC is more effective and if
accommodated with supplementary controller, by
adjusting the equivalent shunt capacitance, SVC
will damp out the oscillations and improves the
overall system stability. The system operating
conditions
change
considerably
during
disturbances. Various approaches are available for
designing auxiliary controllers in SVC. An
attractive feature of fuzzy logic control is its
robustness in system parameters and operating
conditions changes. Fuzzy logic controllers are
capable of tolerating uncertainty and imprecision to
a greater extent. [3]
This method provides rapid damping of system
oscillations for several configurations of the power
systems in about 7 to 8s, whenever the speed
deviation signal is not available, as in the case of
the SVC located at the middle of the transmission
line, the bus bar angle, frequency, power and
current deviation signals can be used. Although the
linguistic controller may be able to provide
significant damping during the transient
disturbances, it may not be robust for all operating
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 1- July 2015
conditions, system parametric changes and noisy
data.
In this paper stability a comparative study has
been made among three cases. First a two machine
system is simulated in Matlab simulink than system
analysis is done during three phase fault.
In the second case static var compensator is
installed for the stability improvement than
simulation results are obtained and a comparison is
made with first case.
The third case is installation of fuzzy logic
controller which is a supplementary coordinator for
SVC. A mamdani method is used fir fuzzy logic
controller, triangular membership functions are
used. Input signal chosen for fuzzy logic controller
is rotor speed deviation and the second input is
derivative of rotor speed deviation, output of fuzzy
logic controller is supplementary voltage. After the
application of fuzzy logic controller simulation
results are compared with above two cases i.e. first
without SVC and second with SVC.
II. METHODOLOGY
A.
Power System Model
Single line diagram of two area system (area1
and area 2).Area 1 is 1000 MW hydraulic
generation plant connected to area2 5000MW
hydraulic generation plant through 500Kv, 700 Km
transmission line. This power system model is
taken from Matlab simulink toolbox.
Fig1 shows simple transmission system
containing 2- hydraulic power plants. SVC has
been used to improve transient stability and power
system oscillations damping. The phasor simulation
method can be used. A single line diagram
represents a simple 500 kV transmission system.
With the development of power system model
A comparative study has been made between three
cases: Case I - System without Static var
compensator.
 Case II - system with static var compensator.
 Case III - System with Fuzzy controlled Static
var compensator.
B.
Static Var Compensator
The SVC is a shunt type of FACTS device
family using power electronics to regulate voltage,
control power flow and improve transient stability
in power system. The SVC regulates voltage at its
terminals by controlling the amount of reactive
power injected into or absorbed from the power
system. The SVC will generates reactive power
(capacitive mode) when the system voltage is low
and will absorbs reactive power (inductive mode)
when the system voltage is high.
The particular SVC modelled in this paper
consists of a thyristor controlled reactor (TCR)
stage to provide the lagging vars and a fixed
capacitor FC which offers the leading vars. The
lagging reactive power (inductive reactive power)
and TCR current amplitude can be controlled
continuously by varying the thyristor firing angle
between 90 and 180. The TCR firing angle can be
fully changed within one cycle of the fundamental
frequency, thus providing smooth and fast control
of reactive power supplied to the system. [1]
The leading vars (capacitive reactive power)
are usually provided by a different number of
capacitor bank units. By combining these two
components, fixed capacitor and continuously
controlled reactor, a smooth variation in reactive
power over the entire range can be achieved and the
sum of the reactive power becomes linear.
So, the TCR-FC can be seen as an adjustable
reactance that can perform both inductive and
capacitive compensation. The reactive power
injection of a SVC connected to a busbar and the
total shunt admittance of the SVC are given by:
...... . . .. (1)
Fig1: Single line diagram of power system model
A 1000 MW hydraulic generation plant (M1)
is connected to a load centre through a long 500
kV, total 700km transmission line. A 5000 MW of
resistive load is modelled as the load centre. The
remote 1000 MVA plant and a local generation of
5000 MVA (plant M2) feed the load. The
transmission line is shunt compensated at its centre
by a 200MVAR Static VAR Compensator (SVC).
ISSN: 2231-5381
In equation (1) QSVC is the reactive power
injection of the SVC (TCR-FC type), BSVC the
admittance of the SVC,
the constant admittance
of the fixed capacitor and
the variable
admittance of the thyristor controlled reactor. For a
TCR-FC compensator the admittance depends on
firing angle α. [7], [8].
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 1- July 2015
The inductive reactance and capacitive reactance
are XL and XC.
C.
SVC V-I Characteristics
The SVC can be operated in two different
modes:
In voltage regulation mode (the voltage is
regulated within limits as explained below).
In VAR control mode (the SVC susceptance is
kept constant).
Fig: 2 V-I Characteristic of SVC
V= Vref + Xs.I: In regulation range (Bcmax<B<Bcmax)
V=I/Bcmax: SVC is fully Capacitive (B=Bcmax)
V=1/Blmax: SVC is fully inductive (B=Blmax)
D.
Fuzzy Logic Controller
In Analytical approaches, Modeling and
Control of Power Network requires mathematical
equations or models. As power system models are
highly non linear, number of assumptions need to
be made before deriving mathematical equations
[7]. Fuzzy Logic is one option by which one can
get rid from above problem because fuzzy logic is
technique which deals with human reasoning that
can be programmed in to fuzzy logic language i.e.
Membership function, rules interpretation [8].
Broadly fuzzy logic controller designed is
classified in to following four states [4]
1.
Fuzzification
2.
Knowledge base
3.
Inference engine
4.
Defuzzification
using membership functions while function of
fuzzy logic engine to infer the proper control
actions based on given fuzzy rules. Under
defuzzification, control actions translated into crisp
values by using normalized membership functions
[9], [10]. In this paper defuzzification of output
signal is done by using centroid method.
Fuzzy logic controller is a good means to control
the parameters when there is not any direct or exact
relation between the input and the output of a
system, and we only have some linguistic relations
in the If-Then form. The use of fuzzy logic has
received increased attention in recent years because
of its usefulness in reducing the need for complex
mathematical models in problem solving, In the
power system area, it has been used in stability
studies, load frequency control, unit commitment,
reactive compensation in distribution networks and
other areas.
This section discusses the basics of the fuzzy
logic control design as applied to the static VAR
compensator. The design of a fuzzy controller can
be resumed to choosing and processing the inputs
and outputs of the controller and designing its four
component elements (the rule base, the inference
mechanism,
the
fuzzification
and
the
defuzzification)
The inputs and the output of the fuzzy system are:
a) The rotor speed deviation dw.
b) Change in speed deviation dw/dt.
c) The output is the supplementary voltage v.
A fuzzy control system is made from different
blocks such as the numeral quantity converter to
fuzzy quantities (fuzzifier interface) block, the
fuzzy logical decision maker section, the
knowledge base section, and the defuzzier interface
block. The following steps are involved in
designing a fuzzy logic controlled static var
compensator.
1. Choose the inputs to the FLC.As shown in fig 4
bellow. Only two inputs, the generator speed
deviation (dw) and the generator speed
derivative (dw/dt), have been employed in this
study. The symbol v is used to represent the
output or decision variable of the FLC.
2. Choose membership functions to represent the
inputs in fuzzy set notation. Triangular
functions are chosen in this study. Fuzzy
representations of the generator speed change,
acceleration, and output variable have been
illustrated.
3. A set of decision rules relating the inputs to the
output are compiled and stored in the memory
in the form of a “decision surface”. The
decision surface is provided in Fig bellow.
Fig 3: Fuzzy logic controller
Function of fuzzification is mapping of input of
fuzzy logic i.e. crisp value in to fuzzy variables by
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 1- July 2015
The logic behind rule can be easily derived.
For exampleR1: if dw is negative big and dw/dt is negative big
than supplementary voltage v is also negative big.
R2: if dw is negative big and dw/dt is negative
small than voltage should be negative big and so
on.
III.
Fig 4: Input membership function dw
SVC Control Scheme An Experimental
Results
SVC fuzzy control diagram for power system
stability enhancement used for simulations is given
bellow. In given model A three phase fault having
clearing time of 0.1 sec is given at 0.1 sec. System
installed without Static Var Compensator it is
observed that system become unstable as shown in
fig bellow.
After that system is installed with static var
compensator of 200MVA it is observed that system
become stable after fault clearance with large
number of oscillations.
After the application of SVC system is
installed with fuzzy logic controller, it is seen from
the bellow Fig that system become stable after
fault, much earlier than the case without svc and
with svc and also have less number of oscillations.
Fig 5: Input membership function dw/dt
Fig 6: Output membership function of V
The logic which is used in this model is given in
rule base table. As the rules given in table means
nb: negative big
ns: negative small
z: zero
ps: positive small
pb: positive big
V
Fig7: Three phase voltage of phase A, B, and C for
the case without SVC
dw/dt
Dw
Nb
Ns
Z
ps
Pb
Nb
Nb
nb
Nb
ns
Z
Ns
Nb
nb
Ns
z
Ps
Z
Nb
ns
Z
ps
Pb
Ps
Ns
z
Ps
pb
Pb
Pb
Z
ps
Pb
pb
Pb
Fig8: Phase A voltage for the three case without
SVC, with SVC
And with fuzzy controlled SVC
Table 1: – Rule base for the fuzzy logic controller
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 1- July 2015
Fig9: Phase B voltage for the three case without
SVC, with SVC And with fuzzy controlled SVC
Fig10: Phase C voltage for the three cases without
SVC, with SVC and with fuzzy controlled SVC
Fig11: Line power P for three cases without SVC,
with SVC, with fuzzy controlled SVC
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Fig12: Rotor angle deviation for the three cases
without SVC, with SVC and with fuzzy controlled
SVC
IV. CONCLUSIONS
The paper presents the mamdani based fuzzy
logic control of a static var compensator for power
system enhancement. Two machine systems were
used for power system configuration and the
simulations and experimental results were obtained
using Matlab-simulink software.
SVC is a FACTS device used to provide
significant damping during transient conditions on
power system. A comparative result made after
simulation between three cases i.e. system without
SVC, system with SVC, and system with fuzzy
controlled SVC.
Form CASE I it is observed that as fault occurs
for 0.1 duration system become unstable after fault
clearance with large magnitude of phase voltage
and it is also seen that large number of oscillations
are present which are very dangerous for the
system.
In CASE II static var compensator is used to
control the stability, from simulation result it is
observed that after clearance of three phase fault
system become stable but oscillations also presents
there and it’s settling time is near about 10 sec
which is shown in Fig.
In CASE III a mamdani based fuzzy logic
controller is installed with conventional SVC.
Simulation results are obtained and it is seen that
oscillations are very much less as compared to
oscillations obtained in the above to cases, and
settling time is also reduced which is less than 10
sec.
Experimental results show that the proposed
mamdani type fuzzy logic controller is more
effective than the conventional static var
compensator for small as well as large scale
disturbances. The fuzzy logic controller has a better
performance with less overshoot during transient
faults.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 1- July 2015
The time domain response under three phase
disturbance shows that the fuzzy controller
provides better damping and in addition mitigates
the model presents in the network as compared to
when SVC only is connected.
REFERENCES
Kundur Prabha, “Power system stability and control.”
McGraw-Hill, 1994.
[2] Hingorani and N.G. Gyungyi, “Understanding FACTS
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[3] Modeling and simulation of static var Compensator fuzzy
control for power system Stability enhancement by
Stelian-emilian oltean, mircea dulău , adrian-vasile duka.
[4] Timothy J Ross, ―Fuzzy Logic with Engineering
Applications, McGraw-Hill, Inc, New York, 1997.
[5] Power quality improvement using fuzzy logic control
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akhtar, shamsudheen.p.m.
[6] Transient Stability Improvement of Two Machine System
using Fuzzy Controlled STATCOM by Surinder
Chauhan, Vikram Chopra, Shakti Singh.
[7] Mohaghegi, S. “An adaptive Mamdani fuzzy logic based
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[8] Zolghardi,
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[9] Timothy J.Ross, “Fuzzy logic with engineering
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[10] Ajami, A. “Application of a Fuzzy Controller for
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[1]
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