Multi-Objective Reactive Power Optimization Based on Opposition-based Learning Cloud Model Adaptive Particle Swarm Optimization

doi:10.11930/j.issn.1004-9649.201705051

Abstract

Abstract: An opposition-based cloud model adaptive particle swarm optimization algorithm (OCAPSO) is presented to solve the high-dimensional problems that the conventional PSO algorithm is easy to fall into a locally optimized point. The algorithm convergence speed is accelerated through opposition-based learning, and the cloud model is used to balance the global and local search ability of each particle, and the adaptive mutation mechanism is used to enhance the population diversity. The effectiveness of OCAPSO is verified by high-dimensional generalized Schwarz function. Then single objective and multi-objective reactive power optimization of IEEE30 bus system are tested. The superiority of OCAPSO is confirmed by comparing with the testing results of PSO and EA. Analysis shows that OCAPSO is effective for multi-objective reactive power optimization.

Key words: reactive power optimization, particle swarm optimization, opposition-based learning, cloud model, adaptive, multi-objective

CLC Number:

CAO Shengrang, DING Xiaoqun, WANG Qingyan, ZHANG Jing. Multi-Objective Reactive Power Optimization Based on Opposition-based Learning Cloud Model Adaptive Particle Swarm Optimization[J]. Electric Power, 2018, 51(7): 21-27.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201705051

https://www.electricpower.com.cn/EN/Y2018/V51/I7/21

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