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Synchronous Generator Excitation System Optimization Control Based on Multi-agent Genetic Algorithm

机译:基于多智能体遗传算法的同步发电机励磁系统优化控制

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Excitation control system of synchronous generator is a strong nonlinearity, multi-variable, strong couple and time-varying control system. It is very difficult for traditional Proportional Integral Derivative (PID) to get good control performance. A new excitation control strategy based on PID controller and Cerebellar Model Articulation Controller (CMAC) is proposed in this study. To solve the problem of PID and CMAC compound controller multi-parameter setting, an Improved Multi-agent Genetic Algorithm (IMAGA) is presented. The PID parameters Kp, Ki, Kd and CMAC parameters η, α are regarded as a agent. Each agent continuously improves its fitness value through competition and cooperation between the other agents according to the objective function of Integral of Time-weighted Absolute value of the Error (ITAE). This algorithm adopts multi-agent coordinate optimization to realize the five parameters of Kp, Ki, Kd, η, αonline tuning. The simulations results show that the compound control scheme based on multi-agent genetic algorithm can improve the precision of excitation control, the speed of responding and has better dynamic and steady-state characteristics.
机译:同步发电机励磁控制系统是一个强非线性,多变量,强耦合和时变的控制系统。传统比例积分微分(PID)很难获得良好的控制性能。提出了一种基于PID控制器和小脑模型关节控制器(CMAC)的励磁控制策略。为了解决PID和CMAC复合控制器的多参数设置问题,提出了一种改进的多智能体遗传算法(IMAGA)。 PID参数Kp,Ki,Kd和CMAC参数η,α被视为代理。每个代理根据错误的时间加权绝对值积分(ITAE)的目标函数,通过其他代理之间的竞争和合作不断提高其适用性值。该算法采用多智能体坐标优化,实现了Kp,Ki,Kd,η,α五个参数的在线调整。仿真结果表明,基于多智能体遗传算法的复合控制方案可以提高励磁控制的精度,响应速度,并具有较好的动态和稳态特性。

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