采用灰色预测控制算法的发电机最优励磁控制

在最优控制策略的基础上,提出了一种基于灰色预测控制算法的发电机最优励磁控制规律。采用GM(1,N)模型对各状态变量进行预测,根据最优控制理论求出含有预测信息的各状态变量的最优反馈增益,从而得到具有预测信息的最优控制量。灰色预测控制理论中灰色建模和“超前控制”的思想较好地弥补了线性最优控制理论中精确线性化和“事后控制”的不足。对单机无穷大系统的仿真结果表明,基于灰色预测控制算法的发电机最优励磁控制规律可明显改善电力系统在大小扰动下的动态特性,并且具有响应速度快的特点。

Optimal Generator Excitation Controller Based on Grey Prediction Control Algorithm

On the basis of optimal control strategy an optimal generator excitation control law based on grey prediction control algorithm is proposed. The state variables are forecasted by GM(1,N) model and according to the optimal control theory the optimal feedback gain of each state variable containing prediction information is solved, thus the optimal control value with prediction information is obtained. The grey modeling and the idea of pre-control in grey prediction control theory can well remedy the defects of exact linearization and after-control in linear optimal control theory. The simulations of single machine infinite bus system show that by use of optimal generator excitation control law based on grey prediction control algorithm the dynamic characteristics of power system under small and severe disturbances can be evidently improved and the response speed of this excitation control law is fast.