基于时变隐马尔科夫模型的连锁故障预测  

Chain failure prediction based on time-varying hidden Markov model

作　　者：宋玉琴[1] 赵攀 周琪玮 李童 Song Yuqin;Zhao Pan;Zhou Qiwei;Li Tong(School of Electronics and Information,Xi'an Polytechnic University,Xi’an 710600,China;Electric Power Research Institute of State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830001,China)

机构地区：[1]西安工程大学电子信息学院,西安710600 [2]国网新疆电力有限公司电力科学研究院,乌鲁木齐830001

出　　处：《电测与仪表》2023年第1期146-153,共8页Electrical Measurement & Instrumentation

基　　金：中国纺织工业联合会科技指导性项目(2018095);陕西省教育厅专项科研计划资助项目(18JK0358);西安市科技计划项目(201805030YD8CG14(17))。

摘　　要：近几年,电力系统大面积停电事故在国内外多有发生,而这些事故都是由连锁故障所引起。因此,分析其发展机理、预测其发展路径成为相关学者重点关注的问题。针对目前预测方法比较单一、不能全面考虑连锁故障的影响因素等问题,文章提出时变隐马尔科夫预测模型,该模型改进了马尔科夫模型中的状态转移矩阵;充分考虑了支路保护/断路器拒动、误动概率;系统支路硬件故障概率;支路使用年限等方面因素。文中基于系统拓扑结构计算支路间的故障状态转移概率矩阵,对该矩阵进行不同的改进,通过对比不同模型的预测结果,优化改进,得到连锁故障传播路径,最后,用IEEE 36模型验证所提方法的可行性和准确性。In recent years,a large area of power system blackouts occur frequently at home and abroad,and these accidents are caused by a chain of faults.Therefore,the analysis of its development mechanism and the prediction of its development path have become the focus of scholars concerned.At present,the prediction method is relatively single and the factors affecting the chain failure cannot be considered comprehensively.In this paper,a time-varying hidden Markov prediction model is proposed,which improves the state transition matrix in the Markov model.Branch protection/circuit breaker rejection,misoperation probability,hardware failure probability of system branch,branch service life and other factors are fully considered.Firstly,the probability matrix of fault state transition between branches is calculated based on the system topology.Secondly,the matrix is improved in different ways.Then,by comparing the predicted results of different models,the chain fault propagation path is obtained through optimization and improvement.Finally,IEEE 36 model is utilized to verify the feasibility and accuracy of the proposed method.

关 键 词：电力系统 大停电事故 马尔科夫 状态转移矩阵 连锁故障预测 

分 类 号：TM74[电气工程—电力系统及自动化]