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Design of Genetic Algorithm Based Fuzzy Logic Power System Stabilizers in Multimachine Power System

机译:基于遗传算法的多机动力系统模糊逻辑电源系统稳定器设计

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This paper presents the design of fuzzy logic power system stabilizers using genetic algorithms in multimachine power system. In the proposed fuzzy expert system, generator speed deviation and acceleration are chosen as input signals to fuzzy logic power system stabilizer. In this approach gains, centers of membership functions and the parameters of the fuzzy logic controllers have been tuned using genetic algorithm. Incorporation of GA in the design of fuzzy logic power system stabilizer will add an intelligent dimension to the stabilizer and significantly reduces computational time in the design process. The problem of selection of optimal parameters of fuzzy logic power system stabilizer is converted into an optimization problem and which is solved by genetic algorithm with the integral of squared time squared error (ISTSE) based objective function. To demonstrate the robustness of the proposed genetic based fuzzy logic power system stabilizer, simulation studies on multimachine system subjected to small perturbation and three-phase fault have been carried out. Simulation results show the superiority and robustness of GA based fuzzy logic power system stabilizer as compare to conventionally tuned controller.
机译:本文介绍了使用多机动力系统遗传算法模糊逻辑电力系统稳定器的设计。在所提出的模糊专家系统中,选择发电机速度偏差和加速度作为模糊逻辑电源系统稳定器的输入信号。在这种方法中,使用遗传算法调整了成员资格函数的中心和模糊逻辑控制器的参数。在模糊逻辑电源系统稳定器设计中将GA融入稳定性尺寸,并显着降低了设计过程中的计算时间。模糊逻辑电力系统稳定器的最佳参数选择的问题被转换为优化问题,并通过遗传算法解决了基于平方时间平方误差(ISTSE)的目标函数的积分。为了证明所提出的基于遗传的模糊逻辑电力系统稳定剂的稳健性,已经进行了对小扰动和三相故障进行的多孔系统的仿真研究。仿真结果表明,GA基于GA的模糊逻辑电源系统稳定器的优势和鲁棒性与传统调谐控制器的比较。

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