电力系统多控制器协调控制与优化  

Coordinated Control and Optimization of Multi-controllers in Power System

作　　者：余宏亮 肖健梅[1] 王锡淮[1] YU Hong-liang;XIAO Jian-mei;WANG Xi-huai(Department of Electrical Automation,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海事大学电气自动化系,上海201306

出　　处：《控制工程》2023年第5期918-926,共9页Control Engineering of China

基　　金：国家自然科学基金资助项目(71771143)。

摘　　要：针对电力系统多控制器协调控制与参数设置较难的问题,提出了一种基于邻域粗糙集属性约简和群搜索优化算法的控制器协调与优化方法。采用群搜索优化算法优化各控制器参数,并在单、双机模型中比较了PSS和SVC独立控制和协调控制效果;通过邻域粗糙集属性约简剔除冗余的优化参数,降低了优化空间维度。实验结果表明,电力系统在SVC和PSS控制下能有效保证系统稳定,且协同优化下的性能表现优于单控制器;群搜索优化算法能有效优化控制器参数,使其达到良好的控制效果;邻域粗糙集属性约简能有效删除冗余属性,在单、双机优化中,待优化参数分别由12个和18个减少到3个和5个,平均优化时间分别节约了68.4%和48.7%。Aiming at the difficulty of multi-controller coordinated controlling and parameter setting in power system,a controller coordination and optimization method based on neighborhood rough sets attribute reduction and group search optimization algorithm is proposed.The group search optimization algorithm is used to optimize the parameters of each controller,and the independent control and coordinated control effects of PSS and SVC are compared in the single and dual machine models;the redundant optimization parameters are eliminated by attribute reduction based on neighborhood rough sets to reduce the optimization space dimension;The experimental results show that the power system under the control of SVC and PSS can effectively ensure the stability of the system,and the performance under collaborative optimization is better than that of the single controller,group search optimization algorithm can effectively optimize the parameters of the controller to achieve good control effect;attribute reduction based on neighborhood rough sets can effectively delete redundant attributes.In single and dual machine optimization,the number of parameters to be optimized is reduced from 12 and 18 to 3 and 5,and the average optimization time is saved by 68%and 48.7%respectively.

关 键 词：电力系统 静止无功补偿器 群搜索优化算法 邻域粗糙集 属性约简 

分 类 号：TM76[电气工程—电力系统及自动化]