An Improved Control Scheme Applied to Static by idesajith

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  An Improved Control Scheme Applied to Static
 Synchronous Series Compensators for Damping of
   Oscillations in Multi-Machine Power Systems
                                                   D.Murali1, Dr.M.Rajaram2
                 1
                  Government College of Engineering/Electrical and Electronics Engineering, Bargur, India
                                            Email: muralitri1@rediffmail.org
               2
                 Government College of Engineering/Electrical and Electronics Engineering, Tirunelveli, India
                                           Email: rajaramgct@rediffmail.com

Abstract— Flexible AC Transmission System (FACTS)                       prevent the transmission interconnections from being fully
devices are widely recognized as powerful controllers to                utilized [3].
improve the dynamic performance. This system allows more                   Power system exhibits various modes of oscillations due
efficient utilization of the existing electricity infrastructure.       to interaction among various components. Most of the
The standard FACTS controllers are linear controllers
designed around a specific operating point from a linearized
                                                                        oscillations are due to synchronous generator rotors
system model with fixed parameters. At other operating                  swinging relative to each other. Stressed power systems are
points or in the event of a major disturbance, those linear             known to exhibit nonlinear behavior. Load changes or
controllers may not guarantee acceptable performance or                 faults are the main causes of power oscillations. If the
stability. A novel approach of artificial intelligence (AI)             oscillations are not controlled properly, it may lead to a
technique called Hybrid Neuro-Fuzzy approach was designed               total or partial system outage. If no adequate damping is
for the external coordinated control of Static Synchronous              available, these oscillations may sustain and grow to cause
Series Compensator (SSSC) based damping controllers. The                system separation [4-6].
advantage of this approach is that it can handle the                       In the past three decades, power system stabilizers
nonlinearities, at the same time it is faster than other
conventional controllers. ANFIS (Adaptive Neuro-Fuzzy
                                                                        (PSSs) have been extensively used to increase the system
Inference System) was employed for the training of the                  damping for low frequency oscillations. The power utilities
proposed fuzzy-logic controllers (FLC). Simulation studies              worldwide are currently implementing PSSs as effective
were carried out in MATLAB/SIMULINK environment to                      excitation controllers to enhance the system stability [7–
evaluate the effectiveness of the proposed nonlinear                    16]. However, there have been problems experienced with
intelligent controllers on multi-machine power systems.                 PSSs over the years of operation. Some of these were due
Results showed that the proposed Neuro-Fuzzy intelligent                to the limited capability of PSS in damping only local and
control has a satisfactory performance during a three-phase             not interarea modes of oscillations. In addition, PSSs can
short circuit fault.                                                    cause great variations in the voltage profile under severe
Index Terms— ANFIS, Coordinated control, Damping
                                                                        disturbances and they may even result in leading power
performance, FACTS, Fuzzy logic, MATLAB/SIMULINK,                       factor operation and losing system stability [17]. This
SSSC, Training of FLC .                                                 situation has necessitated a review of the traditional power
                                                                        system concepts and practices to achieve a larger stability
                      I. INTRODUCTION                                   margin, greater operating flexibility, and better utilization
                                                                        of existing power systems.
   Power systems are continuously expanded and upgraded                    A new concept of flexible ac transmission systems
to cater the ever-growing power demand. Due to limited                  (FACTS) brought radical changes in the power system
energy resources, time and capital required, the present                operation and control. A new technique using FACTS
trend is looking for the new techniques for improving the               devices linked to the improvements in semiconductor
power system performance. The FACTS controllers have                    technology opened new opportunities for controlling power
the ability to control the interrelated parameters, that                and enhancing the usable capacity of existing transmission
govern the operation of transmission system, including                  lines. As supplementary functions, damping the interarea
series impedance, shunt admittance, current, voltage, phase             modes and enhancing power system stability using FACTS
angle and damping of oscillations at various frequencies                controllers have been extensively studied and investigated.
below rated value.                                                      Generally, it is not cost-effective to install FACTS devices
   Furthermore, with increasing power transfer and heavier              for the sole purpose of power system stability enhancement.
loading, power systems become gradually more complex                    Some FACTS devices can control both active and reactive
to operate and they may become less secure for riding out               power. For power system security enhancement, both the
major power outages [1], [2]. As a result, large power                  active and reactive powers should be controlled. This can
flows with inadequate control may be observed and                       be performed with FACTS devices such as Unified power
excessive reactive power and large dynamic swings may                   flow controller (UPFC) and Static synchronous series
be experienced in different parts of the system which will              compensator (SSSC).




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                                             Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 3, Nov 2010


     II. STATIC SYNCHRONOUS SERIES COMPENSATOR                        The performance of the hybrid neuro-fuzzy controllers is
                                                                      compared with the cases of multi-machine power systems
   The SSSC is a power electronic-based synchronous
                                                                      (i).with fuzzy coordinated SSSC controllers, (ii).with
voltage source that generates three phase ac voltages of
                                                                      SSSC, and (iii).without SSSC respectively.
controllable magnitude and phase angle. This voltage,
which is injected in series with the transmission line, is
                                                                                       III. POWER SYSTEM MODEL
almost in quadrature with the line current and hence
emulates an equivalent inductive or capacitive reactance in              The single line diagram of a two-machine power system
series with the transmission line. When the series injected           with SSSC is shown in Fig.2. It consists of two power
voltage leads the line current, it emulates an inductive              generating stations and a 3-phase load at bus B3. The first
reactance causing the power flow and the line current to              power generating station has a rating of 2100 MVA [24]
decrease. When the line current leads the injected voltage,           and the other one has a rating of 1400 MVA. The 3-phase
it emulates a capacitive reactance thereby enhancing the              load of approximately 2200 MW is modeled using a
power flow over the line. The basic schematic diagram of              dynamic load model where the active and reactive power
the static synchronous series compensator with its test               absorbed by the load is a function of the system voltage.
system is shown in Fig.1.                                             The generating substation I is connected to this load by two
   The Voltage-Source Converter (VSC) is the basic                    transmission lines L1 and L2. The line L1 is 280 km long
building block of many of the modern FACTS devices                    and the line L2 is split into two segments of each 150 km
such as STATCOM (Static synchronous compensator),                     length in order to simulate a three-phase fault (using a fault
SSSC, and UPFC. The VSC uses switching gates, that have               breaker) at the midpoint of the line. The generating
turn-on and turn-off capability, such as Gate Turn-Off                substation II is connected to the load by the line L3 of 100
Thyristor (GTO), Insulated Gate Bipolar Transistor                    km length. The SSSC is located between the buses B1 and
(IGBT), MOS Turn-Off Thyristor (MTO) and Insulated                    B2. It has a rating of 100 MVA and is capable of injecting
Gate-Commutated Thyristor (IGCT). The VSC generates                   upto 10% of the nominal system voltage. This SSSC is a
ac voltage from a dc voltage. With a VSC, the magnitude,              typical three-level PWM converter having a nominal
the phase angle and the frequency of the output voltage can           voltage of 40 kV with an equivalent capacitance of 375 μF.
be controlled. It has the capability to transfer power in             On the AC side, its total equivalent impedance is 0.16 p.u.
either direction by just reversing the polarity of the current.       on 100 MVA base. This impedance represents the
   The SSSC using a VSC to inject a controllable voltage              transformer leakage reactance and the phase reactor of the
in quadrature with the line current of a power network, is            IGBT bridge of actual SSSC.
able to rapidly provide both capacitive and inductive                    The SSSC injected voltage reference is normally set by a
impedance compensation independent of the power line                  POD (Power oscillation damping) controller. In general,
current. Moreover, a SSSC with a suitably designed                    the structure of a simple FACTS POD controller is shown
external damping controller [18-19] can be used to                    in Fig.3. It involves a transfer function consisting of an
improve the damping of the low frequency power                        amplification block, a washout block and two lead-lag
oscillations in a power network. These features make the              blocks and an output limiter. Commonly, the local signals
SSSC an attractive FACTS device for power flow control,
power oscillation damping and improving transient
stability.
   An attempt has been made to apply hybrid neuro-fuzzy
(HNF) approach for the coordination between the
conventional power oscillation damping (POD) controllers
for multi-machine power systems. With the




                                                                        Fig. 2 Single line diagram of a two-machine power system with SSSC




                                                                             Fig. 3 The Structure of a simple FACTS POD controller
                Fig. 1 Schematic diagram of SSSC

help of MATLAB, a class of adaptive networks, that are
functionally equivalent to fuzzy inference systems, is
proposed. The proposed architecture is referred to as
ANFIS (Adaptive Neuro-Fuzzy Inference System) [20-23].

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                                              Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 3, Nov 2010


                                                                   performance of the above mentioned damping controllers
                                                                   deteriorates in multi-machine power systems. The damping
                                                                   performance of the FACTS based damping controllers in
                                                                   multi-machine power systems can be improved by using
                                                                   fuzzy coordinated design [26]. The structure of the
                                                                   proposed fuzzy coordination controller is shown in Fig.5,
                                                                   where the inputs PSSSC1 and PSSSC2 are the active power
                                                                   flows through SSSCs connected between Bus2 and Bus3
                                                                   and Bus6 and Bus7 respectively. Thus, the conventional
                                                                   damping controllers were tuned by using fuzzy logic
                                                                   controllers.
                                                                       The fuzzy logic controller [27], as shown in Fig.6,
                                                                   comprises of four stages: fuzzification, a knowledge base,
       Fig. 4 The multi-machine power system configuration         decision making and defuzzification. The fuzzification
of FACTS devices are always applied for the damping                interface converts input data into suitable linguistic values
control. The inputs to the POD controller are the voltage at       that can be viewed as label fuzzy sets. In this paper, the
bus B2 and the current flowing in the line L1.                     inputs are fuzzified into three fuzzy sets: B (big), M
   The compensator is equipped with a source of energy,            (medium) and S (small) as shown in Fig.7. The knowledge
which helps in supplying or absorbing active power to or           base comprises knowledge of application domain and
from the transmission line along with the control of               attendant control goals by means of set of linguistic control
reactive power flow. A 3-machine 9-bus interconnected              rules. The decision making is the aggregation of output of
power system model is simulated in this study. There are           various control rules that simulate the capability of human
two SSSCs in the power system, one connected between               decision making. In this paper, the rules are trained using
Bus2 and Bus3 and another connected between Bus6 and               ANFIS technology. Table 1 shows the rule base of the
Bus7 respectively. The single line diagram of the multi-           fuzzy logic controller. To obtain a deterministic control
machine power system model is shown in Fig.4.                      action, a defuzzification strategy is required.
                                                                   Defuzzification is a mapping from a space of fuzzy control
                IV. DESIGN METHODOLOGY                             actions defined over an output universe of discourse into a
                                                                   space of non-fuzzy (crisp) control actions. There are
                                                                   different techniques for defuzzification of fuzzy quantities
A. Design of damping controller
                                                                   such as Maximum method, Height method, and Centroid
   The damping controller is designed to improve the               method. Here, Centroid method was used for
damping torque. The structure of an SSSC based damping             defuzzification.
controller is shown in Fig.3. It consists of gain, signal
wash-out and phase compensator blocks. The block of
signal wash-out is a high pass filter that modifies the SSSC
input signal and prevents steady changes in active power.
Therefore, TW should have a large value to allow signals
associated with active power oscillations to pass
unchanged. The value of TW is not critical and may be in
the range of 1 to 20 seconds. Here it is assumed to be equal
to 10 seconds. Values of controller parameters were kept
within specified limits. The parameters of the POD
controller were adjusted by trial and error [25]. The POD
controller parameters are given in Appendix.
B. Design of fuzzy logic coordinated damping controller
   Most of the FACTS based damping controllers belong                        Fig. 6 Principle design of fuzzy logic controller
to the PI (Proportional + Integral) type and work
effectively in single machine system [25]. However, the




                                                                                    Fig. 7 Membership Function (mf)
         Fig. 5 SSSC based Fuzzy-Coordination Controller




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                                                  Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 3, Nov 2010

   TABLE I : Decision table (Rule base) with 3 membership functions for       Hence, an adaptive network was constructed, which is
                      each of the two input signals                           functionally equivalent to a fuzzy logic fault locator. The
                                                                              structure can update the membership functions and rule
                                                                              base parameters.
                                                                                  The training procedure is achieved based on the batch
                                                                              learning technique, where the tuning of the fuzzy logic
                                                                              controller is achieved with a back-propagation algorithm
                                                                              using input-output training data set. Considering the
                                                                              computation complexity and the resulting performance,
                                                                              parameters are trained using the gradient descent and the
                                                                              least square estimation (LSE) method.
                                                                                  The membership functions of two inputs of controller
                       V. ANFIS TRAINING
                                                                              represent the triangular membership functions for each
    In this work, both membership functions and the                           linguistic set and each input. The number of epochs is
inference system are optimized using ANFIS technology.                        determined according to the type of membership function
                                                                              and the number of membership functions and to the
A. ANFIS Scheme
                                                                              accepted error measure, fixed by the user. In the present
    In this part, the structure of ANFIS for tuning                           study, 20 epochs have been taken. Based on the training
parameters of the fuzzy inference system with two inputs                      data set, ANFIS [28-30] automatically generates a first-
and one output is explained. The scheme of proposed                           order Sugeno fuzzy type, using only 3 triangular MFs and
ANFIS structure and its application in a multi-machine                        9 rules. ANFIS automatically trains its fuzzy model 20
power system is shown in Fig.8. It consists of five layers:                   epochs. For better results, the number of epochs can be
In layer 1, each node generates membership grades of a                        increased.
linguistic label. In this paper, as shown in Fig7, the
triangular membership functions are selected. Parameters                              V. SIMULATION RESULTS AND DISCUSSION
in this layer are referred to as premise parameters S1 and
they can be trained using the ANFIS learning algorithm.                          The two-machine power system model shown in Fig. 2
Each node in layer 2 is a fixed node and calculates the                       was simulated in Matlab/Simulink environment for the
firing strength of each rule via multiplication of the                        cases of both with and without SSSC based damping
incoming signals. Nodes in layer 3 compute the normalized                     controllers during a three-phase short circuit fault of 200
firing strength of each rule. Each node in layer 4 is an                      milliseconds duration at bus B4 and the simulation results
adaptive node and in this layer parameters of output are                      are shown in Fig.9 and Fig.10. From the simulation results,
adjusted and the output of the ith node is given by equation                  it was inferred that in damping power system oscillations
(1).                                                                          the SSSC based damping controller is more effective than
               f i = ωi ( pi x + qi y + ri )         (1)                      the system without damping controllers.
                                                                                 Then the SSSC based damping controller was made use
where   ωi    is the output of layer 3. ( pi , qi , ri ) are                  of in the three-machine power system model shown in
referred to as the consequent parameter set S2. They can                      Fig.4. A hybrid neuro-fuzzy coordination controller was
also be trained using ANFIS learning algorithms. The layer                    designed following the procedure presented in the above
5 has only one node and it calculates the overall output as a                 section. Sugeno-type fuzzy inference system controller was
summation of all input signals:                                               utilized in the proposed scheme with the parameters inside
                                                                              the fuzzy inference system decided by the neural-network
                       n
                                                                              back-propagation method.
                f =   ∑ fi                                    (2)
                                                                                 To verify the performance of the proposed neuro-fuzzy
                      i =1                                                    controller, a three phase short circuit fault was applied at
                                                                              Bus 3 in the 3-machine 9-bus power system model shown
                                                                              in Fig.4. The duration of the short circuit fault is 200
                                                                              milliseconds. The power system model was simulated in
                                                                              Matlab/Simulink environment for the cases of without
                                                                              SSSCs, with SSSCs, with fuzzy coordinated SSSCs, and
                                                                              with neuro-fuzzy coordinated SSSCs. The system dynamic
                                                                              responses are shown in Figs.11-14 for the above mentioned
                                                                              cases. From the Fig.11, it was inferred that a very poor
                                                                              power angle oscillation damping is observed without using
                                                                              SSSC, during a disturbance. From the Figs.12-14, it is clear
                                                                              that the designed neuro-fuzzy controller is robust in its
                                                                              operation and gives a superb damping performance
                                                                              compared with the other three cases. The surface plot of
                                                                              the ANFIS model is shown in Fig.15. The input1 is the
   Fig. 8 Proposed ANFIS structure for multi-machine power system             reference value of VSe . The input2 is the actual value of


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                                                    Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 3, Nov 2010


VSe . The output1 is the reference value of current from
the PI controller. Besides the simple architecture of the
neuro-fuzzy controller, it has the potentiality of
implementation in real time environment.




                                                                                  Fig. 12 Damping performance of 3-machine power system with SSSC
                                                                                 during a 3-phase short circuit fault of 200 milliseconds duration at Bus 3




  Fig. 9 Damping performance of a two-machine power system without
SSSC based damping controller during a 3-phase short circuit fault of 200
                  milliseconds duration at Bus B4




                                                                                 Fig. 13 Damping performance of 3-machine power system with Fuzzy-
                                                                                        coordinated SSSCs during a 3-phase short circuit fault of
                                                                                                  200 milliseconds duration at Bus 3




   Fig. 10 Damping performance of two-machine power system with
 SSSC based damping controller during a 3-phase short circuit fault of
               200 milliseconds duration at Bus B4




                                                                                 Fig. 14 Damping performance of 3-machine power system with Neuro-
                                                                                      fuzzy coordinated SSSCs during a 3-phase short circuit fault of
                                                                                                   200 milliseconds duration at Bus 3




Fig. 11 Damping performance of 3-machine power system without SSSC
during a 3-phase short circuit fault of 200 milliseconds duration at Bus 3




                                                                                             Fig. 15 Surface plot of proposed ANFIS model




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                                                 Int. J. of Recent Trends in Engineering and Technology, Vol. 4, No. 3, Nov 2010


                         VI. CONCLUSION                                     [15] IEEE Symposium on Eigenanalysis and Frequency Domain
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