中文摘要：

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。

英文摘要：

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.