Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem
Mousa, A., El-Shorbagy, M. (2014). Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem. EKB Journal Management System, 9(9th International Conference on Electrical Engineering ICEENG 2014), 1-24. doi: 10.21608/iceeng.2014.30391
A. A. Mousa; M. A. El-Shorbagy. "Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem". EKB Journal Management System, 9, 9th International Conference on Electrical Engineering ICEENG 2014, 2014, 1-24. doi: 10.21608/iceeng.2014.30391
Mousa, A., El-Shorbagy, M. (2014). 'Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem', EKB Journal Management System, 9(9th International Conference on Electrical Engineering ICEENG 2014), pp. 1-24. doi: 10.21608/iceeng.2014.30391
Mousa, A., El-Shorbagy, M. Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem. EKB Journal Management System, 2014; 9(9th International Conference on Electrical Engineering ICEENG 2014): 1-24. doi: 10.21608/iceeng.2014.30391

Hybrid Particle Swarm Optimization Based Local Search Technique for Reactive Power Compensation Problem

The International Conference on Electrical Engineering
Article 36, Volume 9, 9th International Conference on Electrical Engineering ICEENG 2014, May 2014, Page 1-24  XML PDF (378.29 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2014.30391
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Authors
A. A. Mousa1, 2; M. A. El-Shorbagy2
1Department of Mathematics and Statistics, Faculty of sciences, Taif University.+
2Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Egypt.
Abstract
This paper presents an enhanced particle swarm optimization (PSO) algorithm, which applied to reactive power compensation (RPC) problem. It integrates the merits of both GAs and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which traveling through the search space, during this travel; an evolution of these particles is performed by a PSO coupled with GA to get approximate nondominated solutions. 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. Also In order to study the algorithm performance, the effect of change of the most significant parameters of the proposed approach was studied. 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. Also the results declare that it is quite difficult to find fixed
values for these significant parameters, thus we recommend to develop dynamic version of the proposed approach using any monitoring algorithm.
Keywords
Multiobjective optimization; Reactive power compensation; particle swarm Optimization and local search
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