电力系统控制器参数优化策略

Parameter optimization strategy of power system controller

针对电力系统多控制器参数选取较难的问题,提出一种基于粗糙集和差分进化参数的优化策略。研究建立同时配置电力系统稳定器(PSS)和静态无功补偿器(SVC)的单机和两机电力系统,采用差分进化算法搜索PSS和SVC提高系统性能的最优控制参数,分析比较单机和多机电力系统中控制器参数全优化和经粗糙集约简后优化的系统性能。仿真结果表明,同时配置PSS和SVC的系统具有更好的性能,采用粗糙集约简后的优化结果与全参数优化后相近;与需要优化12个参数的单机系统和18个参数的多机系统相比,经粗糙集约简后,都只需要搜寻3个控制器参数,且能分别节约24%和14.55%的优化时间。粗糙集和差分进化结合的优化策略在满足系统稳定运行的要求下,能够节约优化过程信息存储空间和优化时间。

Aiming at the difficulty of selecting multi-controller parameters in power system,an optimization strategy based on rough set and differential evolution parameters was proposed.Configuration power system stabilizer(PSS)and static var compensator(SVC)of single and two machine power system were researched and established,the differential evolution algorithm was used to search the PSS and SVC to improve system performance of the optimal control parameters,and the system performances of single-machine and multi-machine power systems that are fully optimized and optimized after rough set reduction were analyzed and compared.The simulation results show that the system with simultaneous PSS and SVC configuration has better performance,and the optimization results after rough set reduction are similar to those after full parameter optimization.Compared with a single machine system with 12 parameters and a multi-machine system with 18 parameters,after rough set reduction,only 3 controller parameters need to be searched and the optimization time can be saved by 24%and 14.55%respectively.The optimization strategy combining rough set and differential evolution can save the information storage space and optimization time of the optimization process under the requirement of stable operation of the system.