学习方法整定电力系统非线性控制器参数

THE ADJUSTMENT OF THE PARAMETERS OF POWER SYSTEM NON-LINEAR CONTROLLER BY LEARNING ALGRITHMS

将迭代学习方法、递增式学习方法引入电力系统可控基金项目 :国家自然科学基金 ( 5 963 70 5 0 )、电力工业部、东北电力集团联合资助。串补非线性控制器的参数整定 ,并根据实际电力系统的强非线性和动态过程等特点 ,将这些学习方法加以改进 :将离线迭代学习改进为在线等周期学习 ,再改进为在线非等周期学习 ;将离线递增式学习法的定义在连续集上的目标函数变为“点”目标函数 ,从而可进行在线学习 ;在此基础上 ,为了使学习参数在系统遭受大扰动时有满意的效果 ,在学习方法中利用了非线性特性。改进后的学习方法高效、简便、实用、易行 ,为控制器参数的整定提供了新方法。数字仿真结果表明 :在同样的计算条件下 ,非等周期迭代学习方法优于等周期学习方法 ,递增式学习方法优于非等周期迭代学习方法。控制器采用学习参数将有更好的品质特性 ,具有较好的动态性能和较强的鲁棒性。

In this paper, the iterative and increasing learning algorithms are introduced to the adjustment of the parameters of TCSC nonlinear controller in power systems. According to the characteristics of practical power systems, such as the strong nonlinearity, dynamic process and etc., the learning algorithms are improved: the off line iterative learning is firstly modified into the on line equal periodical learning, and then into the on line unequal periodical learning. By changing the objective function defined in a continuous set into a function in a point, the increasing learning can be on line. In order to make the learning algorithms process satisfied effectiveness and can learn under large disturbances of the system, the nonlinearity of the system is used in the learning algorithms. The improved learning algorithms are efficient, simple and practical, and provide new methods for the adjustment of the parameters of the controller. The digital simulation shows that under the same conditions, the performance of the unequal periodical learning algorithm is better than that of equal periodical learning , the performance of the increasing learning algorithm is better than that of the unequal periodical learning. The parameters of the controller found by the learning algorithms make the controller possess better dynamical performance,strong adaptability and robustness.