Multi-objective VAr Planning with SVC for a Large Power System Using PSO and GA

Particle swarm optimization (PSO) algorithm is used for planning the static VAr compensator (SVC) in a large-scale power system. The primary function of an SVC is to improve transmission system voltage, thereby enhancing the maximum power transfer limit. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem in a large-scale power system is solved by the fuzzy PSO with very encouraging results, and the results are compared with those obtained by the genetic algorithm (GA)

[1]  Kwang Y. Lee,et al.  An automatic tuning method of a fuzzy logic controller for nuclear reactors , 1993, IEEE Transactions on Nuclear Science.

[2]  N.D. Hatziargyriou,et al.  Ant colony system-based algorithm for constrained load flow problem , 2005, IEEE Transactions on Power Systems.

[3]  Hirotaka Yoshida,et al.  A particle swarm optimization for reactive power and voltage control in electric power systems considering voltage security assessment , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[4]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[5]  J.G. Vlachogiannis,et al.  Determining generator contributions to transmission system using parallel vector evaluated particle swarm optimization , 2005, IEEE Transactions on Power Systems.

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  P. Kundur,et al.  Power system stability and control , 1994 .

[8]  H. Sasaki,et al.  A comprehensive approach for FACTS devices optimal allocation to mitigate voltage collapse , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[9]  R. Garduno-Ramirez,et al.  Dynamic multiobective optimization of power plant using PSO techniques , 2005, IEEE Power Engineering Society General Meeting, 2005.

[10]  K.Y. Lee Tutorial on Intelligent Optimization and Control for Power Systems: An Introduction , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[11]  Kwang Y. Lee,et al.  MULTIOBJECTIVE OPTIMAL POWER PLANT OPERATION USING PARTICLE SWARM OPTIMIZATION TECHNIQUE , 2005 .

[12]  C. S. Chang,et al.  Optimal multiobjective SVC planning for voltage stability enhancement , 1998 .

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  Joong-Rin Shin,et al.  A particle swarm optimization for economic dispatch with nonsmooth cost functions , 2005, IEEE Transactions on Power Systems.

[15]  Mohamed A. El-Sharkawi,et al.  Tutorial on Evolutionary Computation Techniques for Power System Optimization , 2003 .

[16]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[17]  Un-Chul Moon,et al.  A self-organizing fuzzy logic controller for dynamic systems using a fuzzy auto-regressive moving average (FARMA) model , 1995, IEEE Trans. Fuzzy Syst..

[18]  Chia-Feng Juang,et al.  Evolutionary fuzzy control of flexible AC transmission system , 2005 .

[19]  Kwang Y. Lee,et al.  Optimization method for reactive power planning by using a modified simple genetic algorithm , 1995 .