Comparison between Genetic and Fuzzy Stabilizer and their effect on Single-Machine Power System
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1 J. Basic. Appl. Sci. Res., 1(11) , , TextRoad Publication ISSN Journal of Basic and Applied Scientific Research Comparison between Genetic and Fuzzy Stabilizer and their effect on Single-Machine Power System Abbas Kargar 1 kargar@ieee.org Ali Misaghi 1* alimisaghi@ymail.com 1 Department of Electrical Engineering, Faculty of Engineering, University of Shahrekord, Shahrekord, Iran ABSTRACT One of the effective measures to increase the dynamic stability, are installed power system stabilizer (PSS) in the generators. PSS are used for raising the overall small signal stability in power systems. In this report, PSS which are used to expression techniques are used by lead-lag block. For the use of PSS in power networks, their parameters must be designed as optimize to reduce the effects of their interaction with each other and substantially increases in damping system. For solving the problem, shall be convert the stabilizer parameters to a simpler optimization problem. This work by an objective function is performed based on Eigen values. This report has attempted to compare a genetic controller with a fuzzy controller. Examining the simulation results and analysis the Eigen values of system, improving of dynamic stability and increasing damping of power system will be visible. KEY WORDS:Genetic algorithm(ga);dynamic stability; Power System Stabilizer (PSS);Fuzzy logic. 1- INTRODUCTION With electrical power systems and promoting their integrity, a new problem was created which one of the problems will appear as sub synchronous oscillations. Applying PSS is considered as an economical method to strengthen a small signal stability of power systems. Additional stabilizing signals can be modulated converter's controllers and controllers of reactive power static compensator for strengthen damping of system oscillations [1]. If the short circuit, switching, load shedding and..., will be occurred as the cause of disturbance and the frequency of network will be some changes which the difference between Synchronous frequency and new frequency is the same with one of the rotational oscillation modes frequencies of power plant. It causes the resonance of axis Oscillatory mode and the electrical torque will be increased and enhance of frequency torsion amplitude on the axis will be occurred. According to the axis limit based on stress tolerance, will lead to fatigue and ultimately failure of axis. The phenomenon of sub synchronous oscillations will be occurring [4]. The various ways to deal with this phenomenon are exist, especially the use of compensator windings, Static blocking filters, HVDC lines, FACTS, [4], and use the exciter control of PSS. Development of power electronics and manufacturing of equipment with current and high voltage, Direct current transmission lines and the elements of flexible alternating current (FACTS) were economically justified. Employing this equipment has many impacts on power systems, including the significant impact that can be cited to improve the dynamic stability [2-3]. In this regard, in power systems may be required two or more methods to improve stability simultaneously, in any case the proposed methods should have economic justification [4]. In this way has been done an acceptable effort in the field of application of PSS [1-2]. Feedback control has been accepted as a major method of power control systems, because in this way, the stabilization objectives are easier to meet. Overall, the feedback control method is performed by the state feedback control method and output feedback control. The optimal control method has been use to reduce the effects of synchronous oscillations [3]. To selection and use matrix to achieve the desired response will be used try and error method. In the control method of poles Placement, the Eigen values are far from the imaginary axis and the response speed is much faster [4-5]. But the blame for this procedure is the probable remove of zero and poles, and the only one operating mode, is effective. Both the above mention methods are the state feedback control methods which require all state variables. Since, access to all power system state variables is not possible, there often is needed to design a state observer. This means, building an effective control system and also limiting the system states, will be more complicated. Hence, state feedback controller in power systems with the Regions of broad coverage, are almost impractical. In order to achieve the harmony combination of PSS parameters is used sequential and concurrent establishing algorithms. In this algorithm, the sequential Eigen value, PSS's parameters, are selected in multi machine power systems [6-7]. The main problem of this method is changed the previous Eigen values. In order to set the PSS's *Corresponding Author: Ali Misaghi, Department of Electrical Engineering,Faculty of Engineering,University of Shahrekord,Shahrekord, Iran. alimisaghi@ymail.com 214
2 Kargar and Misaghi, 211 parameters simultaneously, the numerical optimization techniques, is used to find the optimum solution. The most common methods accepted by most researchers are, the linear programming methods or the gradient reduced [8-9]. In recent years, the genetic algorithm optimization method is provided to help control engineers in the controller s design [1-11]. In order to make efficient traditional methods, the genetic algorithm are used to discover the complex of practical solutions. For further evaluation, single-machine power system is studied. 2- Power system model: single-machine system connected to an infinite Bus In this section to view of PSS s effect, the linear model of Heffron-Phillips is used. Figure (1) is shown the block diagram of Heffron-Phillips model on single-machine system connected to the infinity Bus. Figure1- The Heffron-Phillips model on single-machine system connected to the infinity Bus In the above linear model, the voltage regulator and exciter is also shown. Except for constants K1 to K6, K3 is the only function of the impedance ratio, and the rest are dependent of the active and reactive power loading. The desired system profile is given below. System profiles: In a single machine connected to the infinity Bus model, the system profiles are as follows: 1 k3 G m G fd ms D 1 T k s ka Ga (1) T S 1 A 3- Stabilizer design (PSS) d o 3 Showing the linear model of state space of a power system which includes PSS for generators is expressed as below: d X A X B u d t Y C X (2) X is the vector of the state variables changes, u is the vector of PSS output changes, Y is the vector of PSS input changes. In Figure (2), the structure of PSS is shown of a gain, a high frequency filter, phase compensation units and an output limiter, along with a filter unit [12]. Figure 2- Power system stabilizer The filter unit is used to be avoiding of uniform changes due to the input signal which can be changed the terminal voltage. In terms of oscillations between regional, the high-frequency filter time constant, to reduce 215
3 J. Basic. Appl. Sci. Res., 1(11) , 211 pre-phase modes in the frequency range between regional and minimize opposite reaction between regional modes, usually set on the amount is 3 seconds. To prepare damping for the oscillations, should be considered a PSS with the pre-phase characteristics, to compensate the post-phase between the generator exciter input and the electrical output torque. A pre and post phase compensator block, is used in this section. The number and characteristics of phase compensator units can be corrected in accordance with the design requirements. PSS's gain to reach the optimal damping, without changing the other modes stability and the system transient stability margin, should be set on an appropriate amount. dp dv ref Heffron-Philips Model dp dvt dω Stabilizer Figure 3- System stabilizer on the single-machine system 4- Fuzzy stabilizer Improving the effect of stabilizer based on fuzzy logic is desirable on using the fuzzy logic to control stabilizer and the Heffron-Phillips model. The fuzzy Stabilizer is as follows by sampling the angular velocity changes and the angular velocity changes rate in the fuzzy controller input. angular velocity changes angular velocity deviation Fuzzy Controller Output Signal Figure 4- Fuzzy stabilizer To obtain the Fuzzy Controller laws complex, the extraction different methods and membership functions are selected. The above stabilizer consists of nine fuzzy membership function which form and its width will be designed by try and error. 216
4 Kargar and Misaghi, 211 Input 1 membership Input 2 membership Output membership 1 bn.5 mn sn ze sp mp bp -5 5 input1 1 n.5 ze p input2 1.5 bn mn sn ze sp mp bp output1 Figure 5- Membership functions of fuzzy controller Using the above membership functions and nineteen fuzzy rule in the fuzzy controller selected on the above system are obtained the following results. Error p ze N bn Bn bn ze mn mn mn bn sn ze ze mn Error ze sp ze ze sp mp sp ze mp bp mp mp bp bp bp bp Table1-Fuzzy rules (mamdani) 217
5 J. Basic. Appl. Sci. Res., 1(11) , Simulation results Figure 6- Fuzzy rules In applying a desired Heffron-Philips model, the system goes unstable when apply a step input as the load torque. The stabilizer is used to control the oscillation of this power system. In the power system stabilizer's block, fixed values T1=.1, T2=.1, T=.7 and the values of Kpss 1, 3 and 5 are considering in order to influence fuzzy stabilizer. The Profile of Heffron- Phillips Block is as follows: - Machine's Profile (per unit) rad V t 1.5; T do 7.8; 1 s p 1 q.15 x d x d x q Transmission line (per unit) Re.2 ; X.4 -Exciter's parameters K A 5 ; T A e / 5 -Coefficients K1 to K6 for the above model K1 K2 K3 K4 K5 K Table2-Coefficients 218
6 Kargar and Misaghi, 211 The results of the torque applied at the load in power system with stabilizer are as follows: 4 2 ( t) x Figure 7- Simulated results of torque applied at the load Using the fuzzy controller, the following results will be obtained: PSS Fuzzy1 PSS Fuzzy2 PSS Fuzzy x PSS Fuzzy1 PSS Fuzzy2 PSS Fuzzy Figure 8- Simulated results by using fuzzy controller 219
7 J. Basic. Appl. Sci. Res., 1(11) , 211 The results of classical stabilizer are obtained as follows: 8 x PSS1 PSS2 2 PSS3 PSS PSS1 PSS2 PSS3 PSS Figure 9- Simulated results by using classical stabilizer And applying the genetic algorithm, more acceptable results are obtained as follows: 6 x Figure 1- Simulated results by using Genetic algorithm Figure 11- Optimize Generation by genetic algorithm 22
8 Kargar and Misaghi, Conclusion By comparing the effect of classical, fuzzy and genetic stabilizers is observed that the effect of genetic stabilizer in an optimal operating point is better than the other samples, and the fuzzy stabilizer has better effect on power system performance with regard changes. Yet, the selection of classical stabilizing coefficients, based on try and error, and to improve the performance of classical stabilizer, applying the genetic algorithm will be useful. And the best coefficients will be determined according to the response of the system. In this paper, the design of PSS has been studied with various tricks to increase system dynamic stability. The design of PSS's parameters optimal will be considered and compared by conventional design, try and error experimental, fuzzy logic and finally genetic algorithm. The optimization criterion is oscillation damping speed and also reduces over shoot. According to the obtained results, the genetic algorithm stabilizer has a significant damping on oscillations. REFERENCES [1] E. V. Larsen and D. A. Swann, "Applying Power System Stabilizers Part I, II, III," IEEE Trans. Power Apparatus and systems, vol. Pas-1, no. 6, pp , [2]M. E. Aboul-Ela, A.A. Sallam, J. D. McCalley, and A.A. Foua, Damping Controller Design For Power System Using Global Signals, IEEE Trans. Power Systems, Vol, 11, no. 2, pp , [3] Y.N.Yu and A. M. Moussa, Optimal Power System Stabilization Through Excitation and Governor Control, IEEE Trans. Power Apparatus and System, vol. PAS-91, pp , [4] J.H. Chow and J.J. Sanchez-Gazca, Pole-Placement Designs for Power System Stabilizers, IEEE Trans. Power System, Vol. 4, pp , 199. [5] Y.N. Yu and Q.H.Li, Pole-Placement Power System Stabilizer Design of An Unstable Nine-Machine System, IEEE Trans. Power Systems, Vol. 5, pp , [6] R. J. Fleming, M. A. Mohan, and K. Pavatisam, Selection of Parameters of Stabilizers in Multimachine Power Systems, IEEE Trans. Power Apparatus and Systems, Vol. PAS-1, no. 5, pp , [7] O.H. Abdalla, S.A.Hassan, and N.T.Tweig, Coordinated Stabilization of A Multimachine Power System, IEEE Trans., Power Apparatus and Systems, Vol. PAS- 13, no. 3, pp , [8] C.M. Lim and S. Elangovan, Design of Stabilizers in Multimachine Power Systems, IEE Proceedings, pt. C, Vol. 132, no. 3, pp , [9] M.R. Khaldi, A.K. Sarkar, K.Y. Lee, and Y.M. Park, The Modal Performance Measure For Parameter Optimization of Power System Stabilizers, IEEE Trans. On Energy Conversion, Vol. 8, no. 4, pp , [1] A. Varsek, T. Urbancis, and B. Filipic, Genetic Algorithms in Controller Design and Tuning, IEEE Trans. On System Man and Cybernetics, Vol. 23, no. 5, pp , [11] C. M. Fonseca and P.J. Fleming, Multiobjective Optimal Controller Design With Genetic Algorithms, in CONTROL 94 Conference Publication, Vol. 389, pp [12] Mehdi Karrari,Power Systems Dynamics And Control,Amirkabir University Of Technology 221
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