电网技术2026，Vol.50Issue(6)：2489-2498,10.DOI:10.13335/j.1000-3673.pst.2024.2131
基于离散随机变量相关性重要抽样的电网可靠性评估交叉熵法
Enhanced Cross-entropy Method With Dependent Importance Sampling for Discrete Variables in Composite Power System Reliability Evaluation
摘要
Abstract
The classical cross-entropy method(CEM)typically employs independent importance sampling for discrete random variables(RVs)that represent operational states,such as the"normal"and"failure"states of power devices.While this simplifies CEM,it may limit its performance.This paper introduces an enhanced version with dependent importance sampling for discrete RVs to fully harness CEM's potential.A discrete mixture model(DMM)is proposed to construct the importance sampling probability density function(IS-PDF)for these RVs,with analytical expressions derived for the DMM parameters.The Expectation-Maximization(E-M)algorithm is used to estimate these parameters.The DMM effectively captures discrete RVs' critical joint variation patterns that significantly impact system reliability,leading to more efficient dependent importance sampling.Compared to independent importance sampling,the proposed method improves sampling efficiency for high-risk loss-of-load events caused by the joint failure of multiple devices,thereby accelerating simulation convergence.Reliability evaluation results for the IEEE-RTS79,IEEE-RTS96,and their modified versions demonstrate the superiority of the proposed method over the classical CEM.关键词
交叉熵法/相关性重要抽样/离散随机变量/离散混合模型/电网可靠性评估Key words
cross-entropy method/dependent importance sampling/discrete random variable/discrete mixture model/power system reliability evaluation分类
信息技术与安全科学引用本文复制引用
赵渊,胡家勤,谢开贵,唐俊杰..基于离散随机变量相关性重要抽样的电网可靠性评估交叉熵法[J].电网技术,2026,50(6):2489-2498,10.基金项目
国家自然科学基金项目(52177071).Project Supported by the National Natural Science Foundation of China(52177071). （52177071）
