基于滤子混合协同进化算法的无功优化

The research of reactive power flow based on filter hybrid co-evolutionary algorithms

混合整数非线性规划问题存在于大量工程和管理中,针对此问题提出一种滤子混合协同进化算法.利用滤子技术代替罚函数处理约束条件,采用混合编码和由差分进化算法与遗传算法异构的种群协同解决混合整数变量问题,引入基于平均熵和Logistic混沌初始化增加算法鲁棒性,利用自适应缩放因子和精英交流学习策略构成策略协同,与种群协同耦合,以提高算法搜索能力.以IEEE30节点测试系统进行无功优化为例,仿真结果表明所提出的算法具有全局搜索能力和有效性.

There are abundant mixed-integer nonlinear programming problems in actual engineering and management. For this, a filter hybrid co-evolutionary algorithm is proposed, which utilizes filter technology to deal with constraints instead of penalty function. Hybrid coding and heterogeneous population co-evolution composed of differential evolution and genetic algorithm are proposed for solving mixed-integer. The introduction of the average entropy and Logistic chaos initialization population increases the algorithm＇s robustness. To improve the search capability, the strategy of cooperative composed of adaptive scaling factor strategy and elite exchange learning strategy is presented, which couples with the population co-evolution into the filter hybrid collaborative evolutionary algorithm. The reactive power optimization results of an actual 30-bus power system show that the proposed algorithm possesses global search ability and effectiveness.