Application of MPSO technique to find optimal location and parameter setting of TCSC  

作　　者：王延鹏 蔡兴国 

机构地区：[1]School of Electrical Engineering and Automation,Harbin Institute of Technology

出　　处：《Journal of Harbin Institute of Technology(New Series)》2011年第3期101-105,共5页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the Specialized Research Fund for the Doctoral Program of Higher Education(Grant No. 20050213006)

摘　　要：A new mothod was presented to find the optimal location and parameter setting of Thyristor Controlled Series Compensator (TCSC) to maxmize the transfer capability.Firstly the sensitivity of the transfer capability with respect was described to the line's reactance was described to find the more sensitive lines for installing TCSC,however,the line which has the most sesitivity value is always not the best line for installing TCSC.For solving this problem,the more sensitive m lines were selected as the alternative line group of installing TCSC,and then modified particle swarm optimization (MPSO) was used to find out the optimal location and the optimal parameter settings of TCSC.Particle swarm optimization (PSO) algorithm can results premature convergence.For solving this problem,population entropy and cellular automata were introduced to it.Simulation results of IEEE 30-bus system proved the effectiveness of the method and its application values.A new mothod was presented to find the optimal location and parameter setting of Thyristor Controlled Series Compensator （TCSC） to maxmize the transfer capability. Firstly the sensitivity of the transfer capability with respect was described to the line＇ s reactance was described to find the more sensitive lines for installing TCSC, however, the line which has the most sesitivity value is always not the best line for installing TCSC. For solving this problem, the more sensitive m lines were selected as the alternative line group of installing TCSC, and then modified particle swarm optimization （MPSO） was used to find out the optimal location and the opti- mal parameter settings of TCSC. Particle swarm optimization （PSO） algorithm can results premature convergence. For solving this problem, population entropy and cellular automata were introduced to it. Simulation results of IEEE 30-bus system proved the effectiveness of the method and its application values.

关 键 词：TCSC optimal location and parameter settings modified particle swarm optimization continuation oower flow 

分 类 号：TM715[电气工程—电力系统及自动化]