南京信息工程大学学报2023，Vol.15Issue(6)：712-722,11.DOI:10.13878/j.cnki.jnuist.20230302002
基于窥孔结构LSTM的电力系统跳闸故障诊断
Power system tripping fault diagnosis based on peephole structure LSTM
摘要
Abstract
Tripping is a common fault in power transmission and distribution systems.Protection measures against tripping used to be relaying operation and electrical component action,which have hysteresis in handling tripping faults.Therefore,the prediction of tripping faults plays a vital role in dealing with hidden problems and power recov-ery.Here,a method of power system tripping fault prediction based on multisource time series data is proposed.LSTM is used to extract the time characteristics of multisource data,which alleviates the problem of RNN gradient disappearance on long time series.A peephole connection structure is added to the three-layer grid to enable single units to check the LSTM unit status in the previous stage,thereby strengthening the network timing memory capabili-ty.Then L2 regularization measures such as parameter normalization are used to mitigate the impact of over fitting in fault prediction.Finally,support vector machine classifier is introduced to improve the generalization ability and ro-bustness of the overall model.The experimental data were obtained from relevant institutions of the State Grid of Chi-na.Experiment results show that the proposed method has higher classification accuracy compared with existing data mining methods.The practical application is discussed for its feasibility in actual scenarios.关键词
跳闸/故障诊断/长短时记忆网络/窥孔结构/多源时序数据Key words
tripping operation/fault diagnosis/long short-term memory network(LSTM)/peephole structure/mul-tisource temporal data分类
信息技术与安全科学引用本文复制引用
张平,王鹏展,龚宁,郑征,高晶,张晓东,庄伟..基于窥孔结构LSTM的电力系统跳闸故障诊断[J].南京信息工程大学学报,2023,15(6):712-722,11.基金项目
国家自然科学基金(61972207) （61972207）
国网河南省电力公司科技计划项目(SGTYHT/17-JS-199) （SGTYHT/17-JS-199）
