基于改进差分进化和粒子群混合算法的电力系统最优潮流计算

doi:10.12052/gdutxb.160149

Abstract

Abstract: A hybrid optimization algorithm combining differential evolution and particle swarm optimization is proposed to solve the non-convex programming problem of nonlinear optimal multi-peak of power system optimal power flow. By using the two-species evolutionary strategy, the particle swarm optimization algorithm and the differential evolution algorithm are used to optimize iterations. The information sharing mechanism is used to co-evolve the two populations in the process of optimization. An aging mechanism and a mechanism of elite selection are proposed to dynamically change the lifetime of the particle according to its guiding ability. In the case of lack of bootstrap capability, a polynomial mutation strategy is adopted to produce a competitor to compete with the optimal particle, so that the global optimization ability of the algorithm is enhanced. Simulation results show that the algorithm has high convergence speed and high precision, and it is effective and feasible.

Key words: differential evolution algorithm, particle swarm optimization algorithm, optimal power flow, hybrid algorithm

CLC Number:

TM744

Chen Jing-hua, Qiu Ming-jin, Tang Jun-jie, Tian Ming-zheng, Tan Geng-rui. A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation[J].Journal of Guangdong University of Technology, 2017, 34(05): 22-28.

References 19

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A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation

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