基于分裂最优乘子重要抽样的电网可靠性评估  

Power System Reliability Assessment Using Importance Sampling Method with Splitting Optimal Multiplier

作　　者：王斌[1] 赵渊[1] 刘威[2] 王成亮[1] 刘家伟[1] 

机构地区：[1]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆市400044 [2]山东电力工程咨询院,山东省济南市250001

出　　处：《电力系统自动化》2008年第19期30-34,共5页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(50607021)~~

摘　　要：提出一种按系统中各元件对系统可靠性影响不同而使用不同最优乘子的重要抽样技巧,以加快系统的蒙特卡罗模拟速度。针对影响程度不同,提出基于元件最大容量和无效度的简便的元件重要度识别方法。在对系统进行蒙特卡罗模拟前快速识别出对系统可靠性影响较大的元件,并对它们进行重要抽样;对剩下的元件进行一般的蒙特卡罗抽样。其间,用黄金分割法在系统蒙特卡罗模拟的同时进行最优乘子的求取,得到最优乘子后继续抽样直到满足预定的收敛准则。通过对IEEE-RTS79可靠性测试系统的验算,证明该方法在传统的重要抽样法基础上进一步提高了蒙特卡罗模拟的收敛速度。An improved importance sampling method with splitting optimal multiplier is presented. Through a heuristic identification of the components＇ importance degree to system reliability, the presented technique can significantly accelerate the convergence speed of the Monte-Carlo simulation. The importance identification is realized using the product of a component＇s capacity and its forced outage rate. Before the Monte-Carlo simulation, the importance identification has been carried out and the optimal multipliers for important components have been achieved through golden section searching approach. Then the importance sampling is carried out among these important components while the traditional Monte-Carlo simulation is done among the remaining components. The convergence speed of the presented method is verified using the IEEE-RTS 79 test system.

关 键 词：可靠性评估 蒙特卡罗法 重要抽样法 重要度因子 最优乘子 收敛速度 

分 类 号：TM732[电气工程—电力系统及自动化]