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Improving Design Approach of Fuzzy-Controller in Flexible AC Transmission System
|關鍵字:||閘控串聯電容器;Thyristor-Controlled Series Capacitor;彈性交流輸電系統;粒子群最佳化;基因演算法;合作型粒子群最佳化;Flexible AC Transmission Systems;Particle swarm optimization;Genetic Algorithm;Cooperative Particle-Swarm Optimization||出版社:||電機工程學系所||引用:|| D. N. Ewart, R. J. Koessler, J. D. Mountford, and D. Maratukulam, "FACTS options permit the utilization of the full thermal capacity of AC transmission," IEE Fifth Intemational Conference on AC and DC Power Transmission, Sept., pp. 13-18, 1991.  C. F. Lu and C. F. Juang, "Evolutionary fuzzy control of flexible AC transmission system," IEE Proc. Gener. Transm. Distrib., Vol. 152, No. 4, pp. 441-448, 2005.  L. Xu and S. A. Zaid, "Tuning of thyristor-controlled series capacitors by eigenvalue sensitivity analysis," IEEE Trans. Power Systems, Vol. 1, pp. 696-699, 1993.  A. R. Bergen and V. Vittal, "Power Systems Analysis," Prentice Hall, 2000.  J. D. Glover and M. S. Sarma, "Power System: Analysis and Design," BROOKS/COLE, 2002.  A. 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This thesis proposes five swarm-intelligence optimization algorithms in order to enhance the performance of a fuzzy controller in flexible AC transmission systems (FACTS). The designed fuzzy controller is connected with a thyristor-controlled series capacitor (TCSC) to suppress the low-frequency oscillation effectively and improve the transient situation. To verify the stability of power systems, this thesis uses three fault transient situations in transmission line to simulate the results after suppression. According to the variation of rotation speed, a fuzzy controller determines an approximate series capacitance to achieve a better dynamic response of FACTS. To design the fuzzy controller, five new swarm intelligence optimization algorithms, including F-CPSO-SK, F-CPSO-HK, F-HGAHPSO, F-HGACPSO-SK, and F-HGACPSO-HK, are employed. F-CPSO-SK and F-CPSO-HK are cooperative particle swarm optimization (PSO) algorithms. F-HGAHPSO introduces the concept of the time-varying acceleration coefficients and inertia weight factor into the hybrid of genetic algorithm and PSO (HGAPSO). F-HGACPSO-SK and F-HGACPSO-HK introduce the cooperative framework into HGAPSO. Simulations and comparisons with F-HGAPSO have demonstrated the effectiveness and efficiency of the proposed evolutionary approaches.
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