电力系统自动化2011，Vol.35Issue(3)：33-38,6.
电力系统可靠性评估的自适应分层重要抽样法
A Self-adapting Stratified and Importance Sampling Method for Power System Reliability Evaluation
摘要
Abstract
A new method for power system reliability evaluation called self-adapting stratified and importance sampling (SASIS) is presented. With the SASIS, the system state space is partitioned into one contingency-free state subspace and various contingency order state subspaces. As contingency-free state subspace sampling is completely avoided, the SASIS converges fast in the system with high reliability. The number of sampling is optimally allocated among the contingency order state subspaces and the probability density function is steadily rectified. This method will markedly increase the calculating efficiency while eradicating the problem of low efficiency with the Monte Carlo method in high efficiency systems as reported in the past. Compared with other Monte Carlo methods, the results of the IEEE-RTS test system show that the method proposed is rational and highly effective and free from degradation.This work is supported by Important Zhejiang Science & Technology Specific Projects (No. 2007C11098).关键词
可靠性评估/蒙特卡洛方法/分层抽样/重要抽样Key words
reliability evaluation/ Monte Carlo method/ stratified sampling/ importance sampling引用本文复制引用
王晓滨,郭瑞鹏,曹一家,余秀月,杨桂钟..电力系统可靠性评估的自适应分层重要抽样法[J].电力系统自动化,2011,35(3):33-38,6.基金项目
浙江省重大科技专项资金资助项目(2007C11098). （2007C11098）
