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首页> 外文期刊>Journal of Electrical and Electronics Engineering Research >Design of particle swarm optimization (PSO) based automatic generation control (AGC) regulator with different cost functions
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Design of particle swarm optimization (PSO) based automatic generation control (AGC) regulator with different cost functions

机译:具有不同成本函数的基于粒子群优化(PSO)的自动发电控制(AGC)调节器的设计

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The design methods of fuzzy logic controls (FLC's) based genetic algorithms (GA's) are used to get an optimized and additional control signal to the high voltage direct current (HVDC) systems, through genetic algorithms. The optimal values for the gain factors associated with each of the two fuzzy logic controller inputs and its output is achieved without changing the membership functions or the rule base of the fuzzy logic controller itself. This paper proposes the different methods of using genetic algorithms in the design of fuzzy logic controller's optimal inputs and output called genetic algorithm based fuzzy logic control. The implementation of genetic algorithms in designing an optimal PI-fuzzy logic controller for an HVDC system, applying the new proposed technique is introduced. The results of designing an optimal genetic algorithms based PI-fuzzy logic controller (GA-PI-Fuzzy) are compared with those obtained using the PI-fuzzy logic controller (PI-Fuzzy) and those obtained using genetic algorithm based fixed element control (GA-FEC). In designing the PI-fuzzy logic controller, two factors are used the scaling factor (or the normalized space of variables) and the gain factor (or the actual space of variables).
机译:基于遗传算法(GA)的模糊逻辑控制(FLC)的设计方法用于通过遗传算法为高压直流(HVDC)系统获得优化的附加控制信号。在不更改模糊逻辑控制器本身的隶属函数或规则库的情况下,即可获得与两个模糊逻辑控制器输入及其输出相关联的增益因子的最佳值。本文提出了在设计模糊逻辑控制器的最优输入和输出时使用遗传算法的不同方法,称为基于遗传算法的模糊逻辑控制。介绍了遗传算法在高压直流输电系统最优PI模糊控制器设计中的应用。将基于最优遗传算法的PI模糊逻辑控制器(GA-PI-Fuzzy)的设计结果与使用PI模糊逻辑控制器(PI-Fuzzy)获得的结果和基于遗传算法的固定元素控制(GA)获得的结果进行比较-FEC)。在设计PI模糊逻辑控制器时,使用了两个因子:比例因子(或变量的归一化空间)和增益因子(或变量的实际空间)。

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