PDF《2011 年5月》

第15 卷第5期2011 年5月电机与控制学报ELECTRIC MACHINES AND CONTROL Vol.

15 No.

5 May

2011 一种改进的近似动态规划方法及其在 SVC 的应用 孙健

1 , 刘锋

1 , SI Jennie2 , 郭文涛

1 , 梅生伟

1 ( 1. 清华大学 电力系统国家重点实验室,北京 100084;

2. 亚利桑那州立大学 电机系,美国 坦普

85287 - 5706) 摘要: 近似动态规划的基本思想是通过近似计算代价函数, 从而避免动态规划中的 维数灾 问题.随机选取初值使得近似动态规划方法需要多次的学习才能最终收敛, 极大地限制了在实际系 统中的应用.针对上述问题, 提出一种基于改进 PID 神经网络的直接启发式动态规划算法, 将初始 执行网络与 PID 控制器之间建立起一种等价关系, 因此可以利用已经设计好的 PID 控制器来指导 其初值选取, 从而使算法收敛性大大提高.改进的神经网络与常规 PID 神经网络相比, 结构简单且 具有更好的扩展性, 性能上具有更强的鲁棒性.对4机2区系统的静止无功补偿器附加阻尼控制 进行仿真测试, 仿真结果表明基于改进 PID 神经网络的直接启发式动态规划算法和初值选取方法 的有效性, 并且在部分状态反馈和延时两种情况下有着很好的控制效果. 关键词: 近似动态规划;

直接启发式动态规划;

改进 PID 神经网络;

静止无功补偿器附加阻尼控制 中图分类号: TU

313 文献标志码: A 文章编号: 1007- 449X( 2011) 05- 0095-

08 An improved approximate dynamic programming and its application in SVC control SUN Jian1 , LIU Feng1 , SI Jennie2 , GUO Wen- tao1 , MEI Sheng- wei1 ( 1. State Key Laboratory of Power System,Tsinghua University,Beijing 100084,China;

2. Department of Electrical Engineering,Arizona State University,Tempe

85287 - 5706,USA) Abstract: The main idea of approximate dynamic programming ( ADP) is approximately computing cost function to avoid the curse of dimension. However,it needs many times learning to converge due to the randomly choosing initial weights. So it is greatly limited in the application. This paper presents a direct heuristic dynamic programming ( DHDP) based on an improved proportion integration differentiation PID neural network ( IPIDNN) . This method constructs an equivalent between the initial action network and PID controller. Therefore,well- designed PID controller can guide the initial weights choosing,so that the convergence of this algorithm will be remarkably improved. Moreover,compared with the traditional PID neural network, the configuration of IPIDNN is flexible and easy to expand, as well as a better robust per- formance. The simulation results show the validity of this algorithm and initial weights choosing method by the static var compensator ( SVC) supplementary control in four- machine two- area system. It also has a good performance in the circumstance of partial state feedback and state delay. Key words: approximate dynamic programming;

direct heuristic dynamic programming;

improved propor- tion integration differentiation neural network;

static var compensator supplementary control 收稿日期:

2010 -

06 -

12 基金项目: 国家自然科学基金海外及港澳学者合作研究基金( 50828701) 作者简介: 孙健( 1981―) , 男, 博士研究生, 研究方向为电力系统稳定控制;

刘锋( 1977―) , 男, 博士, 副教授, 研究方向为电力系统稳定控制;

SI Jennie( 1963―) , 女, 博士, 教授, IEEE Fellow, 研究方向为近似动态规划;