基于模糊混合进化算法的多个FACTS元件协调控制

针对多个FACTS装置的控制器之间存在的负交互影响,通过将模糊理论、协同进化算法和免疫算法相结合,提出一种模糊混合多目标进化算法求解FACTS元件的协调控制问题。在求解过程中,利用模糊集理论建立模糊多目标优化模型,确定整体的优化目标。采用协同进化算法对各FACTS元件控制器的参数进行种群划分,以克服常规进化算法在问题规模扩大时易于出现的早收敛现象。在优化过程中,结合多种群合作策略和免疫算法保持解的多样性,最终得到控制参数的Pareto最优解集。对静止同步串联补偿器与静止同步补偿器2种FACTS装置的控制器进行了协调设计,仿真结果表明,与常规进化算法相比,所提方法能同时实现影响分析与参数优化功能,且收敛快速,稳定性好。

Multi-FACTS coordinated control based on fuzzy hybrid evolutionary algorithm

In order to avoid the negative interaction among multiple FACTS controllers.a multi-FACTS coordinated control is presented,which combines the fuzzy theory,co-evolutionary algorithm and immune algorithm together.The fuzzy model is established based on fuzzy set theory to confirm the overall optimization objects,the co-evolutionary algorithm is then applied to group the parameters of FACTS controller for avoiding the early convergent of conventional evolutionary algorithm.and the Pareto optimal solution set of control parameters is finally obtained by adapting the multi-group cooperation strategy and the immune algorithm to keep the variety of antibody during optimization.Two FACTS controllers.the static synchronous series compensator and the static synchronous compensator,are coordinately designed and the simulative result shows that the proposed algorithm realizes the functions of influence analysis and parameter optimization.with faster convergence speed and better stability.