Transient Stability Enhancement of Multi-machine Power System using Fuzzy Controlled TCSC

Transient Stability Enhancement of Multi-machine Power System using Fuzzy Controlled TCSC

1S.Sankara Prasad

PG-Student, Dept. of Electrical & Electronics Engg. SKDEC, Gooty.

2N.Narasimhulu

Associate Professor, Dept. of Electrical & Electronics Engg. SKDEC, Gooty.

3Dr.D.V.Ashok Kumar

Professor in Dept. of Electrical & Electronics Engg & Principal of SDIT (W), Nandyal.

Abstract

Power system is subjected to sudden changes in load levels. Stability is an important concept which determines the stable operation of power system. In general rotor angle stability is taken as index, but the concept of transient stability, which is the function of operating condition and disturbances deals with the ability of the system to remain intact after being subjected to abnormal deviations. For the improvement of transient stability the general methods adopted are fast acting exciters, circuit breakers and reduction in system transfer reactance. The modern trend is to employ FACTS devices in the existing system for effective utilization of existing transmission resources. These FACTS devices contribute to power flow improvement besides they extend their services in transient stability improvement as well. In this paper, the studies had been carried out in order to improve the Transient Stability of WSCC 9 Bus System with Fixed Compensation on Various Lines and Optimal Location has been investigated using trajectory sensitivity analysis for better results. In order to improve the Transient Stability margin further series FACTS device has been implemented. A fuzzy controlled Thyristor Controlled Series Compensation (TCSC) device has been used here and the results highlight the effectiveness of the application of a TCSC in improving the transient stability of a power system.

1. Introduction

   Power system stability has been recognized as an important problem for secure system operation since the 1920s. Many major blackouts caused by power system instability have illustrated the importance of

   this phenomenon. As power systems have evolved through continuing growth in interconnections, use of new technologies and controls, and the increased operation in highly stressed conditions, different forms of system instability have emerged. For example, voltage stability, frequency stability and inter area oscillations have become greater concerns than in the past. This has created a need to review the definition and classification of power system stability. A clear understanding of different types of instability and how they are interrelated is essential for the satisfactory design and operation of power systems. Power system stability is the ability of an electric power system, for a given initial operating condition, to regain a state of operating equilibrium after being subjected to a physical disturbance, with most system variables bounded so that practically the entire system remains intact [2]. The power system is a highly nonlinear system that operates in a constantly changing environment; loads, generator outputs and key operating parameters change continually. When subjected to a disturbance, the stability of the system depends on the initial operating condition as well as the nature of the disturbance. Stability of an electric power system is thus a property of the system motion around an equilibrium set, i.e., the initial operating condition. In an equilibrium set, the various opposing forces that exist in the system are equal instantaneously or over a cycle.

   Power systems are subjected to a wide range of disturbances, small and large. Small disturbances in the form of load changes occur continually; the system must be able to adjust to the changing conditions and operate satisfactorily. It must also be able to survive numerous disturbances of a severe nature, such as a short circuit on a transmission line or loss of a large generator. A large disturbance may lead to structural changes due to the isolation of the faulted elements. At an equilibrium set, a power system may be stable for a given (large) physical disturbance, and unstable for

   another. It is impractical and uneconomical to design power systems to be stable for every possible disturbance [2]. The design contingencies are selected on the basis that they have a reasonably high probability of occurrence. Hence, large-disturbance stability always refers to a specified disturbance scenario. The response of the power system to a disturbance may involve much of the equipment. For instance, a fault on a critical element followed by its isolation by protective relays will cause variations in power flows, network bus voltages, and machine rotor speeds; the voltage variations will actuate both generator and transmission network voltage regulators; the generator speed variations will actuate prime mover governors; and the voltage and frequency variations will affect the system loads to varying degrees depending on their individual characteristics. Further, devices used to protect individual equipment may respond to variations in system variables and cause tripping of the equipment, thereby weakening the system and possibly leading to system instability. If following a disturbance the power system is stable, it will reach a new equilibrium state with the system integrity preserved i.e., with practically all generators and loads connected through a single contiguous transmission system. Some generators and loads may be disconnected by the isolation of faulted elements or intentional tripping to preserve the continuity of operation of bulk of the system. Interconnected systems, for certain severe disturbances, may also be intentionally split into two or more islands to preserve as much of the generation and load as possible. The actions of automatic controls and possibly human operators will eventually restore the system to normal state. On the other hand, if the system is unstable, it will result in a run-away or run-down situation; for example, a progressive increase in angular separation of generator rotors, or a progressive decrease in bus voltages. An unstable system condition could lead to cascading outages and a shutdown of a major portion of the power system.

   Power systems are continually experiencing fluctuations of small magnitudes. However, for assessing stability when subjected to a specified disturbance, it is usually valid to assume that the system is initially in a true steady-state operating condition.

2. System Configuration with TCSC

   Fig.1. Test system: WSCC 9-bus system (Western System Coordinating Council)

   Fig.1. shows a test system of WSCC 9-bus system with TCSC controller to performing transient stability improvement. It consists of the series compensating capacitor shunted by a Thyristor-Controlled Reactor. In a practical TCSC implementation, several such basic compensators may be connected in series to obtain the desired voltage rating and operating characteristics.

   Fig.2. Equivalent circuit of TCSC

   The TCSC model is given in Fig.2. The overall reactance XC of the TCSC is given in terms of the firing angle as

   Let us denote the fundamental frequency capacitance of the TCSC, which is equal to 1/(sXC), as Ctcsc. It is to be noted that in this paper the TCSC is operated only in the capacitive mode. The capacitive reactance XFC of the TCSC is chosen as half of the reactance of the line in which the TCSC is placed and the TCR reactance XP is chosen to be 1/3 of XFC.

   A three-phase fault is simulated in one of the lines of the nine-bus system. The simulation is done in three steps. To start with, the pre-fault ystem is run for a small time. Then, a symmetrical fault is applied at one end of a line. Simulation of the faulted condition continues till the fault is cleared after a time tcl. Then, the post-fault system is simulated for a longer time (say 5 s) to observe the nature of the transients. The fault may be of self-clearing type (i.e. isolation of line is not

   In the case of power system, sensitivity of state variables, e.g., the generator rotor angle ( ) and per unit speed deviation ( r ) can be computed as in

   (3.3) with respect to some parameter . Now one of the generators, say the jth one, is taken as the reference. Then, the relative rotor angle of the ith machine (i.e. the

   excursion of ij with respect to the rotor angle of

   required for fault clearance) or may be cleared by

   reference machine) is given by

   ij i

   j . The

   isolating the faulted line.

3. Trajectory Sensitivity Analysis

   sensitivity of ij with respect to is computed as

   1. Computation of Trajectory Sensitivity

      ij i j

      (4)

      Multi machine power system is represented by a set of differential equations

      The sensitivity of relative rotor angle is considered here instead of the sensitivity of of an individual machine because the relative rotor angle is the relevant factor

      x f (t, x, ),

      x(t0 ) x0

      (1)

      when angular stability is concerned.

      Where x is a state vector and is a vector of system parameters. The sensitivities of state trajectories with respect to system parameters can be found by perturbing from its nominal value 0 . The equations of trajectory sensitivity can be found as,

      Normalized (ETA) values of a Nine Bus System for different fault locations

      x f x

      x

      f ,

      x (to ) 0

      (2)

      Where x x /

      . Solution of (1) and (2) gives the

      state trajectory and trajectory sensitivity, respectively. However sensitivities can also be found in a simpler way by using numerical method.

   2. Numerical Evaluation: Alternative to Reduce Computation

      To explain this method, let us choose only one parameter, i.e., becomes a scalar and the sensitivities with respect to it are studied. Two values of are chosen (say 1 and 2 ). The corresponding state vectors x1 and x2 respectively are then computed.

      Now the sensitivity at 1 is defined as

4. Fuzzy Controller Model

   Fuzzy modeling is the method of describing the characteristics of a system using fuzzy inference rules. The method has a distinguishing feature in that it can express linguistically complex non-linear system. It is however, very hand to identify the rules and tune the membership functions of the reasoning. Fuzzy Controllers are normally built with fuzzy rules. These fuzzy rules are obtained either from domain experts or by observing the people who are currently doing the control. The membership functions for the fuzzy sets

   x x x

   (3)

   will be derive from the information available from the

   Sens 2 1

   2 1

   If is small, the numerical sensitivity is expected to be very close to the analytically calculated trajectory sensitivity.

   domain experts and/or observed control actions. The building of such rules and membership functions require tuning. That is, performance of the controller must be measured and the membership functions and rules adjusted based upon the performance. This process will be time consuming.

   Fig.2. Structure of Fuzzy Logic controller

   The basic configuration of Fuzzy logic control based as shown in Fig.2. consists of four main parts i.e. (i) Fuzzification, (ii) Knowledge base, (iii) Inference Engine and (iv) Defuzzification.

5. Fuzzy controller Fuzzy inputs:

   Input 1 : ERR(t) = (Pref(i)-Pflow(i)) Inpur 2 : CHERR(t)=ERR(t)-ERR(t-dt)

   Fuzzy outputs:

   Output: Xtcsc (t) (compensation to be provided 30- 70%)

   Rule base for fuzzy controller

6. Results and Discussion

   By comparing the above results we can conclude that, with TCSC Controller incorporated in the line 6-9 for a fault at bus 5. This shows the improvement of Transient Stability with FUZZY controller over PI Controller and there is a significant improvement in the Transient Stability with variable series Compensation.

   Case (1) Fault is at Bus 5

   1. fault is of self clearing type and it is at bus 5 and fault cleared time is 0.2sec and with fixed

      compensation 50% compensation and peak value of first swing is 61.3

   2. Fault is of self clearing type and it is at bus 5 and fault cleared time is 0.2sec With PI Controller (initial compensation 50% with (KP=0.5 and Ki = 6.5) and the

      first swing is 59.65.

   3. With Fuzzy Controller, the System, with fault clearing time 0.2sec the first swing is 36.88 deg.

   Case (1I) Fault is at Bus 6

   1. fault is of self clearing type and it is at bus 6 and fault cleared time is 0.2sec and with fixed

      compensation 50% compensation and peak value of first swing is 52.61

   2. Fault is of self clearing type and it is at bus 6 and tcl= 0.2sec with PI Controller (initial compensation 50% with KP=0.5 and Ki = 6.5) and the first swing

      45.47.

   3. With Fuzzy Controller, the System, with fault clearing time 0.2sec the first swing is 24.52 deg.

   Case (II1) Fault is at Bus 8

   1. Here fault is of self clearing type and it is at bus 8 and fault cleared time is 0.2sec and with fixed compensation 50% compensation and peak value of first swing is 64.02

   2. Fault is of self clearing type and it is at bus 8 and fault cleared time is 0.2sec With PI Controller (initial compensation 50% with KP=0.5 and Ki = 6.5) and the

      first swing 54.69

   3. With Fuzzy Controller, the System, with fault clearing time 0.2sec the first swing is 35.83 deg

7. Conclusion

   Transient stability is the ability of the power system to maintain synchronism after subjected to severe disturbance. The synchronism is assessed with relative rotor angle violations among the different machines. Accurate analysis of the transient stability requires the detailed modelling of generating units and other equipment. At present, the most practical available method of transient stability analysis is time-domain simulation in which the nonlinear differential equations are solved by R.K. fourth order method or network reduction techniques. In the present work, the transient stability assessment of WSCC-9 bus system is carried out for three phase fault of self clearing type at different fault locations. When effect of damping of the system is incorporated the analysis shows better results. Further, a TCSC controller has been modelled and implemented on the WSCC-9 bus system at the optimal location. The effective location of TCSC for different faults locations is obtained by performing trajectory sensitivity analysis with respect to clearing time. The case studies depicts the optimal location of fixed compensation in the WSCC- 9 bus system as line 5-7, based on the stability index(ETA). In the steady state, FACTS controllers like TCSC help in controlling the power flow through a line. Since power systems are non-linear, conventional controllers PI can not perform well in maintaining power system stability. When firing angle of TCSC is controlled using conventional PI controller reduction in first swing peak value is observed when compared to fixed compensation. Further, a fuzzy controlled TCSC has been implemented on WSCC-9 bus system to improve stability of system. The fuzzy controlled TCSC is observed to perform better compared to conventional PI controller.

8. Reference

1. P. Kundur, Power System Stability and Control,

   McGraw- Hill, Inc., 1994

2. Prabha Kundur, John Paserba, Definition and Classification of Power System Stability, IEEE Trans. on Power Systems., Vol. 19, No. 2, pp 1387- 1401 May 2004.

3. Stagg and El- Abiad, Computer Methods in Power System Analysis, International Student Edition, McGraw- Hill, Book Company, 1968.

4. K. R. Padiyar, HVDC Power Transmission Systems, New Age International (P) Ltd., 2004.

1. Dheeman Chatterjee, Arindam Ghosh, TCSC control design for transient stability improvement

   of a multi-machine power system using trajectory sensitivity, Department of Electrical Engineering, Indian Institute of Technology, Kanpur 208 016,

   India

2. P.W. Sauer, M.A. Pai, Power System Dynamics and Stability, Prentice Hall, Upper Saddle River, 1998.

3. Dheeman Chatterjee , Arindam Ghosh*, Application of Trajectory Sensitivity for the Evaluation of the Effect of TCSC Placement on Transient Stability International Journal of Emerging Electric Power Systems, Volume 8, Issue 1 2007 Article 4, The Berkeley Electronic Press

S.Sankara Prasad was born in Andhra Pradesh, India in 1981. He received the B.Tech (Electrical and Electronics Engineering) degree from JNTU University, India in 2009 and the M.Tech (Electrical Power Systems) pursuing from same University. In 2010 (November) he joined the Dept. Electrical and Electronics Engineering, S.K.D.College of Engineering and Technology, Gooty, as a PG-Student.

e-mail:sankaraprasadsalanku@gmail.com

Mr. N. Narasimhulu has completed his professional career of education in B.Tech (EEE) at JNTU Hyderabad in the year 2003. Later he successfully completed M.Tech in EPE in 2008 from JNTU Hyderabad. His keep interests and special focus his in POWER SYSTEMS. From 2003-2008 he has worked as Assistant Professor and at present working as Associate Professor and Head of the EEE Department in SKD Engg College, Gooty of Anantapur district (AP). He has published two papers and attempts for further progress in technical field. He is a life Member of Indian Society for Technical Education (India).

Dr. D. V. Ashok Kumar, is graduated in 1996, Masters in 2000 from J.N.T.U.C.E, Anantapur and Ph.D in 2008 from the same university. He worked 12 years at R.G.M. College of Engineering Technology, Nandyal, A.P. in the cadars of Assistant Professor, Professor and Head of Electrical and Electronics Engg. Department. Since 2008 to till date he is working as Principal of Syamaladevi institute of Technology for women, Nandyal. He has published 15 research papers in national and international conferences and journals. He has attended 10 National & International workshops. His areas of interests are Electrical Machines, Power Systems & Solar Energy. He is a member of IEEE, I.S.T.E, K.D.T.F & SESI.

E-mail: principal.sdit@gmail.com

International Journal of Engineering Research & Technology (IJERT)

ISSN: 2278-0181

Vol. 1 Issue 6, August – 2012