基于遗传算法的窄间距隶属函数在模糊逻辑电力系统稳定器设计中的应用

提出了一种简单、高效的模糊逻辑电力系统稳定器的设计方案,其特点是采用了窄间距隶属函数(SSMFs).SSMFs是设计模糊逻辑控制器隶属函数的一种新方法.采用遗传算法选择SSMFs模糊逻辑控制器的最佳参数值,包括间距系数、重叠系数和增益系数等.通过单机无穷大系统验证所提方案的正确性.结果表明,所提出的SSMFs模糊逻辑电力系统稳定器在发电机负载和励磁增益大范围变化情况下,改善了电力系统的稳定性.

Application of Shrinking Span Membership Functions Based on Genetic Algorithms in Design of Fuzzy Logic Power System Stabilizer

An efficient and simple scheme of design of fuzzy logic power system stabilizer was presented. The essential feature of the proposed scheme is that the shrinking span membership functions(SSMFs) is used for structuring membership functions. The SSMFs is new means of designating membership functions of a fuzzy logic controller(FLC). Genetic algorithm was used to select optimum values for SSMFs FLC parameters namely shrinking, overlapping and gain factors. A single machine connected to an infinite bus system through a transmission line was considered to demonstrate the suggested technique. The result validate the effectiveness of the proposed SSMFs fuzzy logic power system stabilizer in enhancing the power system stability over a wide range variation of generator loading conditions and a variety of exciter gain.