1) This document presents a new fuzzy-coordination controller for coordinating FACTS controllers like UPFCs to damp oscillations in a multi-machine power system. 2) The controller first optimizes the parameters of traditional POD controllers for the FACTS devices, then uses a fuzzy logic controller to coordinate the inputs of the POD controllers based on power flows. 3) Digital simulations show the new fuzzy-coordination controller improves damping performance over a range of disturbances and operating conditions, and is robust to changes in system parameters. It coordinates the FACTS controllers to damp both local and inter-area oscillations in the power system.

1. 1
Fuzzy Coordination of FACTS Controllers for
Power systems
ELECTRICAL & ELECTRONICS ENGINEERING
K.Sravani Srinija (3/4 EEE) G.Annapurneswari (3/4 EEE)
Email: sravanisrinija@gmail.com Email: alekyagupta@yahoo.co.in
Ph: 9963160279 Ph: 9290196174
Narasaroapet Engineering College

2. 2
ABSTRACT
This paper concerns the of a multi-machine power system subjected to
optimization and coordination of the a wide variety of disturbances and different
conventional FACTS (Flexible AC structures validate the efficiency of the new
Transmission Systems) damping controllers in approach.
multimachine power system. Firstly, the
parameters of FACTS controller are Keywords:
optimized. Then, a hybrid fuzzy logic
controller for the coordination of FACTS FACTS,
controllers is presented. This coordination Fuzzy Logic,
method is well suitable to series connected Coordination,
FACTS devices like UPFC, TCSC etc. in Fuzzy- Coordination Controller,
damping multi-modal oscillations in multi- Damping,
machine power systems. Digital simulations Stability

3. 3
other power system controllers is very
important.
Fuzzy-coordination controller is
1. INTRODUCTION
presented in this paper for the coordinated of
Nowadays, FACTS devices can be
traditional FACTS controllers. The fuzzy
used to control the power flow and enhance
logic controllers are rule-based controllers in
system stability. They are playing an
which a set of rules represents a control
increasing and major role in the operation and
decision mechanism to adjust the effect of
control of power systems. The UPFC (Unified
certain cases coming from power system.
Power Flow Controller) is the most versatile
Furthermore, fuzzy logic controllers do not
and powerful FACTS device .The parameters
require a mathematical model of the system.
in the transmission line, i.e. line impedance,
They can cover a wider range of operating
terminal voltages, and voltage angle can be
conditions and they are robust.
controlled by UPFC. It is used for
This paper focuses on the
independent control of real and reactive power
optimization of conventional power
in transmission lines. Moreover, the UPFC
oscillation damping (POD) controllers and
can be used for voltage support and damping
fuzzy logic coordination of them. By using
of electromechanical oscillations. In this
fuzzy-coordination controller, the
paper, a multimachine system with UPFC is
coordination objectives of the FACTS devices
simulated.
are quite well achieved.
Damping of electromechanical
oscillations between interconnected
2. SYSTEM MODEL
synchronous generators is necessary for
secure system operation. A well-designed
2.1. Power System Model
FACTS controller can not only increase the
transmission capability but also improve the
A three machine nine bus interconnected
power system stability. A series of approaches
power system is simulated in this paper. There
have been made in developing damping
are two UPFCs in the power system: between
control strategy for FACTS devices. The
Bus2 Bus3 and, Bus6 Bus7. The diagram of
researches are mostly based on single machine
the power system model is shown in Fig. 1.
system. However, FACTS devices are always
installed in multi-machine systems. The
coordination between FACTS controllers and

4. 4
1) VAR control mode: the reference input is
an inductive or capacitive Var request;
2) Automatic voltage control mode: the
goal is to maintain the transmission line
voltage at the connection point to a reference
value.
By the control of series voltage, UPFC can be
operated in four different ways
2.2. UPFC Model (UPFC Theory) 1) Direct voltage injection mode: the
Basically, the UPFC have two reference inputs are directly the magnitude
voltage source inverters (VSI) sharing a and phase angle of the series voltage;
common dc storage capacitor. It is connected 2) Phase angle shifter emulation mode: the
to the system through two coupling reference input is phase displacement between
transformers. One VSI is connected in shunt the sending end voltage and the receiving end
to the system via a shunt transformer. The voltage;
other one is connected in series through a 3) Line impedance emulation mode: the
series transformer. The UPFC scheme is reference input is an impedance value to insert
shown in Fig. 2. in series with the line impedance;
4) Automatic power flow control mode: the
reference inputs are values of P and Q to
maintain on the transmission line despite
system changes.
Generally, for damping of power system
oscillations, UPFC will be operated in the
direct voltage injection mode. The mathematic
model of UPFC for the dynamic simulation is
shown in Fig.3
The UPFC has several operating modes. Two
control modes are possible for the shunt
control:

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3. CONTROL SCHEME 3.2. POD Controller
Commonly the POD controllers
3.1. Traditional FACTS Damping Control involve a transfer function consisting of an
Scheme amplification link, a washout link and two
Under a large disturbance, line lead-lag links. A block diagram of the
impedance emulation mode will be used to conventional POD controller is illustrated in
improve first swing stability. For damping of Fig. 5. In this paper the active power of the
the subsequent swings, as suggested before, transmission line is used as input signal
UPFC will be operated in the direct voltage
injection mode. In this mode, the UPFC
output is the series compensation voltage V
se. This voltage is perpendicular to the line
current I line and the phase angle of I line is
ahead of V se. Thus, as shown in Fig.4, the
damping control of the UPFC is the same as a The UPFC POD controller works
TCSC POD control scheme. By the control of effectively in single machine system. In order
the magnitude of V se, the series to improve the dynamic performance of a
compensation damping control can be multi-machine system, the behavior of the
achieved. controllers must be coordinated. Otherwise
the power system will be deteriorated.
3.3. Fuzzy Logic Control
In order to keep the advantage of the
existing POD controller and to improve its

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control performance in multimachine systems, installed. This objective can be formulated as
the hybrid fuzzy coordinated controller is the minimization of a nonlinear programming
suggested in this paper. problem expressed as follows:
Fuzzy logic controller is one of the
most practically successful approaches for
utilizing the qualitative knowledge of a
system to design a controller .In this paper the
main function of the fuzzy logic control is to where f(x) is the objective function, x are the
coordinate the operation of FACTS parameters of the POD controller. A(x) are
controllers. In section 4 the design of the the equality functions and B(x) are the
fuzzy logic coordinated controller is presented inequality functions respectively. Particularly
in detail. B(x) indicate the restrictions of the POD
parameter. (i.e. the restrictions of lead-lag
links and wash-out links). In this simulation,
only the inequality functions B(x) are
4. PARAMETER OPTIMIZATION AND necessary.
CONTROLLER DESIGN
The objective function is extremely important
4.1. Parameter Optimization for a Single for the parameter optimization. In this paper
Machine POD Controller the objective function is defined as follows:
In order to work effectively under
different operating conditions, many
researches are made on the controller
parameter optimization. Parameters of the where, δ(t, x) is the power angle curve of the
POD controller can be adjusted either by trial generator and t1 is the time range of the
and error or by optimization technique. In this simulation. With the variation of the
paper the parameters of the POD controller controller parameters x, the δ(t, x) will also be

are optimized using a nonlinear programming changed. The power system simulation
algorithm. program PSD (Power System Dynamic) is
Originally, the aim of the employed in this simulation to evaluate the
parameter optimization is to damp oscillations performance of the POD controller.
of power systems where the UPFCs are

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Equation (1) is a general parameter- system. Therefore the coordination between
constrained nonlinear optimization problem POD controllers must be taken into account.
and can be solved successfully. In this paper
the Matlab Optimization Toolbox is applied.
The optimization starts with the pre-
selected initial values of the POD controller.
Then the nonlinear algorithm is used to
iteratively adjust the parameters, until the
objective function (2) is minimized. These so
determined parameters are the optimal
settings of the POD controller.
The flow chart of the parameter
optimization is shown in Fig. 6 the proposed
optimization algorithm was realized in a
single machine power system. In this
optimization the prefault state and post-fault
state are the same where δ(0)= δ(∞) . The
optimized parameters are given in Appendix To cope with the coordination
2. problem, the optimization based coordination
and the feedback signal based coordination
4.2. Fuzzy Logic Coordinated Controller have been developed. Also fuzzy logic has
Design successfully been applied to coordination. The
Most of the FACTS POD controllers method used in is using the fuzzy logic
belong to the PI (proportional integral) type controller to coordinate the input signal of the
and work effectively in single machine FACTS controller.
system. Especially, after the parameter In this paper the fuzzy logic
optimization, the damping of power system controller is to coordinate the parameters of
oscillations is perfectly achieved. However FACTS controllers. The structure of the
the performance of the above mentioned POD proposed fuzzy-coordination controller is
controllers deteriorates in multi-machine shown in Fig. 7. Where the inputs P UPFC1
and P UPFC2 are the active power flow

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through the UPFC1 and UPFC2. The output
signals are command signals adjusted to the
UPFC controllers 1 and 2. In this way, the
conventional POD controllers are tuned by
using fuzzy-coordination controllers. The
fuzzy coordination controller involves
Fuzzification, Inference and Defuzzification
unit.
The membership function of the small set is:
Where x, namely P UPFC1 or P UPFC2, is
the input to the fuzzy controller. Similarly the
big set membership function is:
and the medium set membership function is:
4.2.1 Fuzzification
Fuzzification is a process whereby
the input variables are mapped onto fuzzy
variables (linguistic variables). Each fuzzified
variable has a certain membership function. The parameters L and K, as shown in
The inputs are fuzzified using three fuzzy Appendix 3, are determined basing upon the
sets: B (big), M (medium) and S (small), as rated values of UPFCs. These parameters can
shown in Fig. 8.

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also be optimized by using the simulation The output of the fuzzy-coordination
results. controller is
4.2.2 Inference
Control decisions are made based on
the fuzzified variables. Inference involves
rules for determining output decisions. Due to
the input variables having three fuzzified where i u corresponds to the value of control
variables, the fuzzy-coordination controller output for which the membership values in the
has nine rules for each UPFC controller. The output sets are equal to unity.
rules can be obtained from the system
operation and the knowledge of the operator. 5. SIMULATION RESULTS
Table 1 shows the inference system. 5.1. Parameter optimization
To determine the degree of The parameter optimization is made in
memberships for output variables, the Min- single machine system. Fig. 9 demonstrates
Max inference is applied. Both of the two the improvement in damping of power system
UPFC controllers use the same inference oscillation. The initial and optimized values of
system. Only the inputs of them are the POD controller are given Appendix 2. Fig.
exchanged. (as shown in Fig. 7) 9. Parameter optimization in a single machine
infinite bus system.
4.2.3 Defuzzification
The output variables of the inference
system are linguistic variables. They must be
converted to numerical output. The fuzzy-
coordination controller uses centroid method.

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5.2. Simulation in multi-machine system
Using the multi-machine power
system shown in Fig. 1, different disturbances
and different network parameters are
simulated. The performance of the fuzzy-
coordination controller for UPFC in damping
power system oscillations is examined. The
following simulations are made for evaluating
the performance of the proposed controller. In
Case 2: Changing of operation conditions
this paper machine G3 is taken as the
(Three-phase fault at Bus 3)
reference.
To validate the robustness of fuzzy-
Case 1: Three-phase fault at Bus 2
coordination controller the pre-fault operating
conditions of the power network is changed to
A three-phase fault of 100 ms duration
P1=0.195, P2=0.28. Moreover the fault type is
is simulated at Bus 2. Fig. 10 presents the
also different: a three-phase fault of 110 ms
results of the examined power system with
duration is simulated at Bus 3. Fig. 11 shows
fuzzy-coordination controller. From Fig. 10 it
the results of the simulation. The proposed
can be seen that with the proposed controller,
controller acts pretty well with the variation of
the dynamic performance of the power system
operation condition.
is quite improved. The pre-fault operating
condition (in p.u.) is: P1=0.105, P2=0.185.

11. 11
Case 3: Changing of network parameters
(Three-phase fault at Bus 9)
In order to verify the performance of
the fuzzy coordination controller for the
changing of system parameters, the reactance
of transformers T1 and T2 are increased by
20%. A three-phase fault of 100 ms is
simulated at Bus 9.
The simulation results, as
shown in Fig. 12, illustrate that the proposed
controller is robust in parametric change. The
pre-fault operating condition (in p.u.) is:
P1=0.10, P2=0.120.

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6. CONCLUSIONS
The paper presents a new fuzzy-
coordination controller for the FACTS
devices in a multi-machine power system to
damp the electromechanical oscillations. The
fuzzy coordination controller is designed
based on the conventional POD controllers.
The amplification part of the conventional
controller is modified by the fuzzy
coordination controller. The performance of
the proposed method is simulated over a wide
range of operating conditions and
disturbances and its robustness is proved.
Both inter-area and local modes oscillations
are quite damped using this new controller.
The proposed control scheme adopts the
advantages of the conventional POD
controller and it is not only robust but also
simple and being easy to be realized in power
system.
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• HVDC Transmission and distribution
systems by Gupta,
• V. Sitnikov, W. Breuer, D. Povh, D.
Retzmann, E. Teltsch, benefits of
Powe r electronics for
• Transmission Enhancement・
• Load-Flow Analysis with Respect to a
possible synchronous Interconnection
of Networks of
• UCTE and IPS/UPS.