AM  Vol.3 No.10 A , October 2012
Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem
ABSTRACT
This paper presents an enhanced Particle Swarm Optimization (PSO) algorithm applied to the reactive power compensation (RPC) problem. It is based on the combination of Genetic Algorithm (GA) and PSO. Our approach integrates the merits of both genetic algorithms (GAs) and particle swarm optimization (PSO) and it has two characteristic features. Firstly, the algorithm is initialized by a set of a random particle which traveling through the search space, during this travel an evolution of these particles is performed by a hybrid PSO with GA to get approximate no dominated solution. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC.

Cite this paper
A. Mousa and M. El-Shorbagy, "Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem," Applied Mathematics, Vol. 3 No. 10, 2012, pp. 1276-1284. doi: 10.4236/am.2012.330184.
References
[1]   P. R. Sujin, T. Ruban Deva Prakash and M. Mary Linda, “Particle Swarm Optimization Based Reactive Power Optimization,” Journal of Computing, Vol. 2, No. 1, 2010, pp. 73-78.

[2]   F. Yoshikazu, “Comparative Studies of Particle Swarm Optimization Techniques for Reactive Power Allocation Planning in Power Systems,” IEEE Transactions on Power and Energy, Vol. 124, No. 5, 2004, pp. 690-696. doi:10.1541/ieejpes.124.690

[3]   J. Lin, X. D. Wang and W. H. Zheng, “Reactive Power Optimization Based on Adaptive Immune Algorithm,” International Journal of Emerging Electric Power Systems, Vol. 10, No. 4, 2008, pp. 1499-1503.

[4]   P. Kundur, “Power System Stability and Control,” Mc Graw-Hill, New York, 1993.

[5]   X. Zhang, W. Chen, C. Dai and A. Guo, “Self-Adaptive Differential Evolution Algorithm for Reactive Power Optimization,” Fourth International Conference on Natural Computation, Jinan, 18-20 October 2008, pp. 560-564. doi:10.1109/ICNC.2008.355

[6]   S. K. Nandha Kumar and Dr. P. Renuga, “Reactive Power Planning Using Real GA Comparison with Evolutionary Programming,” International Journal of Recent Trends in Engineering, Vol. 1, No. 3, 2009.

[7]   D. Thukaram and G. Yesuratnam, “Optimal Reactive Power Dispatch in a Large Power System with AC-DC and FACTS Controllers,” IET Generation, Transmission & Distribution, Vol. 2, No. 1, 2008, pp. 71-81. doi:10.1049/iet-gtd:20070163

[8]   J. Carlisle, A. El-Keib, D. Boyd and K. Nolan, “A Review of Capacitor Placement Techniques on Distribution Feeders,” Proceedings of the 29th Southeastern Symposium on System Theory, Cookeville, 9-11 March 1997, pp. 359-365. doi:10.1109/SSST.1997.581664

[9]   M. Delfanti, G. Granelli, P. Marannino and M. Montagna, “Optimal Capacitor Placement Using Deterministic and Genetic Algorithms,” Proceedings of the 21st 1999 IEEE International Conference, Vol. 15, No. 3, 2000, pp. 1041-1046.

[10]   J. Lin and X. Wang, “Reactive Power Optimization Based on Adaptive Immune Algorithm,” International Journal of Emerging Electric Power Systems, Vol. 10, No. 4. 2009, pp. 1499-1503. doi:10.2202/1553-779X.2079

[11]   Y. Liu, L. Ma and J. Zhang, “Reactive Power Optimization by GA/SA/TS Combined Algorithms,” International Journal of Electrical Power & Energy Systems, Vol. 24, No. 9, 2002, pp. 765-769. doi:10.1016/S0142-0615(01)00087-4

[12]   J. Lu, L. Zhang, H. Yang and J. Du, “Improved Strategy of Particle Swarm Optimisation Algorithm for Reactive Power Optimisation,” International Journal of Bio-Inspired Computation, Vol. 2, No. 1, 2010, pp. 27-33.

[13]   J. T. Ma and L. L. Lai, “Evolutionary Programming Approach to Reactive Power Planning,” IEEE Proceedings of Generation, Transmission and Distribution, Vol. 143, No. 4, 1996, pp. 365-370. doi:10.1049/ip-gtd:19960296

[14]   K. Mahadevan and P. S. Kannan, “Comprehensive Learning Particle Swarm Optimization for Reactive Power Dispatch,” Applied Soft Computing Archive, Vol. 10, No. 2, 2010, pp. 641-652. doi:10.1016/j.asoc.2009.08.038

[15]   M. A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem,” IEEE Translation on Evolutionary Computation, Vol. 10, No. 3, 2006, pp. 315-329. doi:10.1109/TEVC.2005.857073

[16]   C. H. Antunesa, D. F. Pires, C. Barrico, á. Gomesa and A. G. Martinsa, “A Multi-Objective Evolutionary Algorithm for Reactive Power Compensation in Distribution Networks,” Applied Energy, Vol. 86, No. 7-8, 2009, pp. 977-984. doi:10.1016/j.apenergy.2008.09.008

[17]   M. Azzam and A. A. Mousa, “Using Genetic Algorithm and TOPSIS Technique for Multiobjective Reactive Power Compensation,” Electric Power Systems Research, Vol. 80, No. 6, 2010, pp. 675-681. doi:10.1016/j.epsr.2009.10.033

[18]   K. Sindhya, A. Sinha, K. Deb and K. Miettinen, “Local Search Based Evolutionary Multiobjective Optimization Algorithm for Constrained and Unconstrained Problems,” 2009 IEEE Congress on Evolutionary Computation, Trondheim, 18-21 May 2009, pp. 2919-2926. doi:10.1109/CEC.2009.4983310

[19]   K. Harada, J. Sakuma, I. Ono and S. Kobayashi, “Local Search for Multiobjective Optimization: Pareto Descent Method,” Proceedings of the 18th Symposium on Decentralized Autonomous Systems of the Society of Instrument and Control Engineers, 2006, pp. 345-350.

[20]   R. Hooke and T. A. Jeeves, “Direct Search Solution of Numerical and Statistical Problems,” Journal of the ACM, Vol. 8, No. 2, 1961, pp. 212-229. doi:10.1145/321062.321069

[21]   H. Dommel and W. Tinney, “Optimal Power Flow Solutions,” IEEE Translation on Power Apparatus and Systems, Vol. 87, No. 10, 1968, pp. 1866-1876. doi:10.1109/TPAS.1968.292150

[22]   J. D. Glover and M. Sarma, “Power System Analysis and Design,” PWS Publishing Company, Boston, 1994.

[23]   K. Deb, “Multi-Objective Optimization Using Evolutionary Algorithms,” Wiley, New York, 2001.

[24]   K. Miettinen, “Non-Linear Multiobjective Optimization,” Kluwer Academic Publisher, Dordrecht, 2002.

[25]   D. E. Goldberg, “Genetic Algorithms in Search, Optimization & Machine Learning,” Addison-Wesley, Reading, 1989.

[26]   M. Tanaka, “GA-based Decision Support System for MultiCriteria Optimization,” IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, 1995, pp. 1556-1561.

[27]   M. F. Bramlette, “Initialization Mutation and Selection Methods in Genetic Algorithms for Functions Optimization,” Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, 13-16 July 1991, pp. 100-107.

[28]   A. A. Mousa and I. M. El-Desoky, “GENLS: Co-Evolutionary Algorithm for Nonlinear System of Equations,” Applied Mathematics and Computation, Vol. 197, No. 2, 2008, pp. 633-642. doi:10.1016/j.amc.2007.08.088

[29]   R. Zimmerman and D. Gan, “MATPOWER: A Matlab Power System Simulation Package,” 1997. http://vivo.cornell.edu/display/AI-33123309569

 
 
Top