元件可靠性参数是电力系统可靠性评估的基础和关键，一旦有误或缺失，就可能导致错误的评估结论，使得评估结果毫无意义。可靠性参数常通过历史停电记录的统计分析得到。但是，由于统计工作的复杂性、动态性及人为因素的影响，数据错误和遗漏不可避免。当前，其已成为影响可靠性评估工程应用的棘手问题。.本项目从可靠性评估的逆向过程入手，研究如何从已知可靠性指标出发求取/校核元件可靠性参数。首次提出电力系统可靠性评估逆问题的概念；基于场景分析，结合串并联等效、网络等值等技术，建立等值的逆问题解析模型；基于可靠性参数与可靠性指标间的单调变化规律，提出逆问题初始解可行区间的估计和压缩分割方法；基于可靠性跟踪或灵敏度分析离散化待求参数，将逆问题转化为等值的组合优化模型求解。.通过项目研究，希冀建立电力系统可靠性评估逆问题的完整理论框架体系，为规划、运行、可靠性管理及设备改造和招投标等提供新的分析方法和量化决策依据。

Component reliability parameters are the foundation and key part in power system reliability evaluation. It may lead to wrong evaluation conclusions if they are incorrect or missing, making the evaluation result meaningless. Reliability parameters are mainly obtained from the statistical analysis of historical blackout records. Due to the influences of complex and dynamic statistical work and human factor, data errors and omissions are inevitable, which currently have become problematic issues affecting the engineering application of reliability evaluation..Starting with the inverse process of reliability evaluation, this project explores the method to obtain/verify component reliability parameters based on known reliability indices. It first proposes the concept of inverse problem of power system reliability evaluation. Based on scenario analysis, this project combines series-parallel equivalent, network equivalent and other technologies to establish analytical model of equivalent inverse problem. Based on the monotonic variation rule of reliability parameters and reliability indices, it proposes the method to estimate, compress and segment the feasible interval of inverse problem’s initial solution. Based on reliability tracing and sensitivity analysis, it discretizes the unknown parameters and the inverse problem is thus converted to equivalent combination optimization model for solution..The research project is expected to establish a complete theoretical framework for the inverse problem of power system reliability evaluation, and to provide new analysis methods and quantitative decision-making basis for planning, operation, reliability management, equipment transformation and bidding.