城市轨道交通牵引变电所开关柜运行状态量预测与故障推断
汪理1张亦然1宋大治1苏许俊2邹大云3白思宁4
Operation State Quantity Prediction and Fault Inference of Urban Rail Transit Traction Substation Switchgear
WANG LiZHANG YiraniSONG DazhiiSU XujuniZOU DayuniBAI Siningi
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作者信息:1.南京地铁建设有限责任公司， 210012， 南京
2.中铁电气化勘测设计研究院有限公司， 300250， 天津
3.南京南瑞继保电气有限公司， 211102， 南京
4.国网辽宁省电力有限公司沈阳供电公司， 110003， 沈阳
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Affiliation:Nanjing Metro Construction Co., Ltd., 210012, Nanjing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.06.023
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中图分类号/CLCN:U231.8
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栏目/Col:研究报告
摘要:
城市轨道交通牵引变电所开关柜故障直接关系供电系统运行安全，一旦发生故障，会直接影响城市轨道交通系统的安全稳定运行，严重情况下甚至会引发重大安全事故。为科学指导开关柜维护，有效降低运行风险，需要准确预测开关柜运行状态变化趋势并实现故障推断。提出一种基于长短期记忆神经网络的方法来预测开关柜各状态量变化趋势，并借助鲸鱼优化算法对长短期记忆神经网络模型进行参数优化，采用灰色关联分析的方法对牵引变电所开关柜状态量预测值和故障进行关联度分析。基于南方某城市轨道交通牵引变电所开关柜的历史运营数据，利用模型进行预测分析，并将预测信息提供给运维人员，指导开关柜维修决策。结果表明，提出的基于鲸鱼优化算法-长短期记忆神经网络的方法能够准确预判开关柜故障，为城市轨道交通牵引供电设备运行提供可靠保障。
Abstracts:
Faults of urban rail transit traction substation switchgear directly affect the security and steady operation of power supply system, also the safe and stable operation of urban rail transit system in case of fault, and even lead to major safety accidents in serious cases. Therefore, accurately predicting the change trend of switchgear operation state and realizing fault inference can scientifically guide the maintenance of switchgear and effectively reduce operation risks. A method based on LSTM (long short term memory) to predict the change trend of each switchgear state quantity is proposed, and WOA (whale optimization algorithm) is adopted to optimize LSTM model parameters. Grey Relation Analysis method is used to analyze the correlation degree between the predicted value of the traction substation switchgear state quantity and the fault. Based on the historical operation data of the switchgear in a southern city urban rail transit traction substation, the model is used for predictive analysis, and the information is provided to the operation maintenance personnel to guide the maintenance decision of the switchgear. The results show that the proposed method based on WOA-LSTM can accurately predict the switchgear faults and provide reliable guarantee for the operation of urban rail transit traction power supply equipment.
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