Information Technology Journal

Year: 2013 | Volume: 12 | Issue: 20 | Page No.: 5731-5735
DOI: 10.3923/itj.2013.5731.5735

A New Variation Particle Swarm Optimization for Multi-objective Reactive Power Optimization

Wenqing Zhao and Liwei Wang

Abstract: In order to overcome the particle swarm algorithm easy to fall into local optimal value and the lack of late slow convergence, this study presents a cloud model based on adaptive particle swarm optimization algorithm. The algorithm according to the fitness value of the particle populations of particles into the near optimal values closer to the optimal value and away from the optimal value of three subgroups and the generation of different populations to adopt a different strategy to generate inertia weight, where the normal cloud generator algorithm uses adaptive dynamic adjustment closer to the optimal particle subgroups of inertia weight, get rid of the shackles of algorithms into local optimum value; in the iteration algorithm uses the normal cloud to the mutation operation of the particle which makes the algorithm can quickly converge to the optimal solution. In summary presented Could Adaptive Variation Particle Swarm Optimization (CAVPSO) to solve the multi-objective optimization problem of reactive power. Use standard IEEE30 node system to test simulation results show that the use of CAVPSO algorithms to solve multi-objective optimization of reactive power superiority.

Keywords: Multi-objective reactive power optimization, cloud model theory, particle swarm optimization algorithm and fuzzy logic

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