PID Parameters Determination of Synchronous Machine AVR System
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This paper presents the use of Genetic Algorithms (GA) for determining optimal parameters of PID controller in synchronous machine automatic voltage regulator (AVR) system. Responses to step changes of AVR system input voltage value, which PID parameters are determined by the use of GA, have been compared to the responses of system which PID parameters are obtained by classic Zeigler-Nichols method. Special emphasis is put on analyzing the impact of the number of GA populations and generations on the values of PID controller parameters, as well as on the quality of the system response. It is shown that GA-PID controller could create perfect step response of the AVR system, as well as that the GA-PID controller is much better than the ZN-PID controller.
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I. Boldea, “Synchronous generators,” (CRC Press Taylor & Francis Group, 2006, 1st edn.)
M. Ostojić, M. Ćalasan, “How far is going electrical power of synchronous machine,” II Symposium CG-KO CIGRE, Budva, Montenegro, Vol. 2, pp. 1-8, Budva, may 2011.
Khaleghi Kerahroudi, S., Zobaa, A.F., Dynamic performance of the wind synchronous generators with different types of excitation systems, (2011) International Review on Modelling and Simulations (IREMOS), 4 (6), pp. 2802-2810.
M. Ćalasan, “Simulation model and dynamics of static excitation system of synchronous generator in HPP Perućica,” Master thesis, Dept. Elect. Eng., University of Montenegro, Podgorica, Montenegro, 2010.
Mazhar, R.M., Tashakori, H., Kar, N.C., Performance analysis of a synchronous generator under loss of excitation, (2008) International Review of Electrical Engineering (IREE), 3 (2), pp. 371-377.
Bulić, N., Sumina, D., Mišković, M., A comparison of advanced control structures for synchronous generator excitation control, (2010) International Review of Electrical Engineering (IREE), 5 (2), pp. 473-480.
IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Std 421.5-1992, IEEE, New-York, NY, USA, 1992.
IEEE Transactions on Power Apparatus and Systems, Excitation system models for power system stability studies, Vol. PAS-100, No. 2, February 1981.
A.N. Abd Alla, “Simulation Model of Brushless Excitation System,” American Journal of Applied Sciences, Vol. 4, Issue 12, 2007, pp.1079-1083.
J.G. Ziegler, N.B. Nichols, “Optimum Settings for Automatic Controllers,” Trans. ASME, Vol. 64: 759-768, 1942.
Duman, S., Öztürk, A., Robust design of PID controller for power system stabilization by using real coded genetic algorithm, (2010) International Review of Electrical Engineering (IREE), 5 (5), pp. 2159-2170.
R. Malhotra, N. Singh, Y. Singh, “Genetic Algorithms: Concepts, Design for Optimization of Process Controllers,” Computer and Information Science, Vol. 4 (Issue 2): 39-54, 2011.
Jalilvand, A., Aghmasheh, R., Khalkhali, E., Optimal design of PID power system stabilizer in multi-machine power system using PSO and genetic algorithm, (2011) International Review of Electrical Engineering (IREE), 6 (2), pp. 907-912.
Z.L. Gaing, “A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System,” IEEE Transactions on Energy Conversion, Vol. 19 (Issue 2): 684-391, june 2004.
L.D.S. Coelho, “Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach,” Chaos, Solutions and Fractals, Vol. 39 (Issue 1): 1504-1514, 2009.
V. Milankov, Evolution biology, (University of Novi Sad, Serbia,2010)
A.J. Chipperfield, P.J. Fleming, H. Pohlheim, C.M. Fonseca, “A Genetic Algorithm Toolbox for MATLAB,” Proc. International Conference on Systems Engineering, Coventry, UK, September, 1994.
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