Different Types of Inference Method for Fuzzy Power System Stabilizer Analysis
DOI:
https://doi.org/10.11113/elektrika.v16n3.65Keywords:
Power system stabilizer, low-frequency oscillation, fuzzy controller, 2 machines 3 buses system, Matlab/SimulinkAbstract
In this paper, fuzzy power system stabilizer (FPSS) is being analyzed. Power system stabilizer (PSS) is acknowledged in stability performance in power system by providing a damping signal for low-frequency oscillation. The application of fuzzy logic controller into power system stabilizer is being presented in the simulation of 2 machines 3 buses environment. The rules of fuzzy is constructed and the performance is being tested for different types of inference method applied to the FPSS. A type of contingency, single phase fault is being tested to validate the ability of the FPSS to overcome the oscillation and improve the stability of the system. The changes in rotor angles as well as the speed of each machine are being measured as the output responses of the FPSS. The simulation of the system is performed in MATLAB/SIMULINK environment. The superior responses for FPSS for both inference methods prove the capability of fuzzy controller to improve the stability of the systemReferences
Rajeev Gupta, D.K. Sambariya and Reena Gunjan. Fuzzy Logic based Robust Power System Stabilizer for Multi-Machine Power System. IEEE International Conference on Industrial Technology; 15-17 December 2006; India. pp. 1037-1042.
E.V. Larsen and D.A. Swann. Applying Power System Stabilizers Part I: General Concepts. IEEE Transaction on Power Apparatus and Systems. 100(6), pp. 3017-3024.
Neeraj Gupta and Sanjay K. Jain. Comparative Analysis of Fuzzy Power System Stabilizer Using Different Membership Functions. International Journal of Computer and Electrical Engineering. 2(2): 262-267.
Lokman H. Hassan, M. Moghavvemi, Haider A.F. Almurib, K.M. Muttaqi and H. Du. Damping of Low-frequency Oscillations and Improving Power System Stability via Auto-tuned PI Stabilizer Using Takagi-Sugeno Fuzzy Logic. International Journal of Electrical Power and Energy Systems. 38(2012): 72-83.
Saeid Kyanzadeh, Malihe M. Farsangi, Hossein Nezamabadi-pour, and Kwang Y. Lee (2007). Design of Power System Stabilizer Using Immune Algorithm. International Conference on Intelligent Systems Applications to Power Systems, 2007. ISAP 2007; 5-8 November 2007; Taiwan. pp. 1-6.
Vandai Le, Xinran Li, Yong Li, Yijia Cao and Caoquyen Le. Optimal placement of TCSC using controllability Gramian to damp power system oscillations. Int. Trans. Electr. Energ. Syst. (2015).
Charu Sharma, Barjeev Tyagi. Ranking of phasor measurement units based on control strategy for small-signal stability. Int. Trans. Electr. Energ. Syst. 2015; 25:2359–2375.
Yashar Hashemi, Rasool Kazemzadeh, Mohammad Reza Azizian, Ahmad Sadeghi Yazdankhah. Concurrently attuned design of a WADC-based UPFC PSDC and multi input PSS for improving power system dynamic performance. Turk J Elec Eng & Comp Sci 2014; 22: 243 - 261
Jakub Osmic, Mirza Kusljugic, Elvisa Becirovic, Daniel Toal. Analysis of active power control algorithms of variable speed wind generators for power system frequency stabilization. Turk J Elec Eng & Comp Sci 2016; 24: 234 - 246
Sheng-Kuan Wang, Ji-Pyng Chiou, and Chih-Wen Liu. Parameters Tuning of Power System Stabilizers using Improved Ant Direction Hybrid Differential Evolution. International Journal of Electrical Power and Energy Systems. 31(2009): 34-42.
Z. Bouchama and M. N. Harmas. Optimal Robust Adaptive Fuzzy Synergetic Power System Stabilizer Design. Electric Power System Research. 83(2012): 170-175.
Lokman H. Hassan, M. Moghavvemi, Haider A.F. Almurib, K.M. Muttaqi, and H. Du. Damping of low-frequency oscillations and improving power system stability via auto-tuned PI stabilizer using Takagi-Sugeno fuzzy logic. International Journal of Electrical Power & Energy Systems. 38(2012): 72-83.
P Kundur, Power System Stability and Control. New York: McGraw Hill, 1994, pp. 669-770.
Jang, J.-S. R. and C.-T. Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, 1997.
Mamdani, E.H. and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, Vol. 7, No. 1, 1975, pp. 1-13.
Sugeno, M., Industrial applications of fuzzy control. Elsevier Science Pub. Co., 1985.
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