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文章摘要
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
Received:May 20, 2020  Revised:May 20, 2020
DOI:10.19753/j.issn1001-1390.2023.01.021
中文关键词: 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
英文关键词: power system, blackout accident, Markov, state transition matrix, chain fault prediction
基金项目:中国纺织工业联合会科技指导性资助项目(2018095);称陕西省教育厅专项科研计划资助项目(18JK0358);西安科技计划资助项目(201805030YD8CG14(17))
Author NameAffiliationE-mail
Song Yuqin School of Electronics and Information, Xi'an Polytechnic University, Xi’an 710600, China 81308995@qq.com 
Zhao Pan* School of Electronics and Information, Xi'an Polytechnic University, Xi’an 710600, China 782637690@qq.com 
Zhou Qiwei School of Electronics and Information, Xi'an Polytechnic University, Xi’an 710600, China 1031670057@qq.com 
Li Tong Electric Power Research Institute of State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830001, China 1310315320@qq.com 
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中文摘要:
      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。
英文摘要:
      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.
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