基于循环神经网络的变电站继电保护二次回路隐藏故障在线检测

孙士强; 刘瑞圣; 姜楠; 庄雷明; 徐磊; 王明锦; 魏玉玺

孙士强,刘瑞圣,姜楠,庄雷明,徐磊,王明锦,魏玉玺.基于循环神经网络的变电站继电保护二次回路隐藏故障在线检测[J].电测与仪表,2026,63(8):180-191.
Sun shiqiang,Liu ruisheng,Jiang nan,Zhuang leiming,Xu lei,Wang mingjin,Wei yuxi.Online detection of hidden faults in secondary circuits of substation relay protection based on recurrent neural networks[J].Electrical Measurement & Instrumentation,2026,63(8):180-191.

基于循环神经网络的变电站继电保护二次回路隐藏故障在线检测

Online detection of hidden faults in secondary circuits of substation relay protection based on recurrent neural networks

DOI：10.19753 / j.issn1001-1390.2026.08.019

中文关键词: 智能变电站继电保护装置二次回路循环神经网络在线检测

英文关键词:Intelligent substation Relay protection device Secondary circuit Recurrent neural network Online detection

基金项目:国家电网有限公司科技项目(5226BJ220005)

作者中文名	作者英文名	单位
孙士强	Sun shiqiang	国网山东电力有限公司临沂供电公司
刘瑞圣	Liu ruisheng	国网山东电力有限公司临沂供电公司
姜楠	Jiang nan	国网山东电力有限公司临沂供电公司
庄雷明	Zhuang leiming	国网山东电力有限公司临沂供电公司
徐磊	Xu lei	国网山东电力有限公司临沂供电公司
王明锦	Wang mingjin	国网山东电力有限公司临沂供电公司
魏玉玺	Wei yuxi	国网山东电力有限公司临沂供电公司

摘要点击次数: 7

中文摘要:

二次回路涉及大量的电气参数和信号,这些数据可能包含噪声,且相互之间存在复杂的关联性,故障往往不易被直接观察到,其时序性难以提取,导致隐藏故障检测难度较大。为此,提出基于循环神经网络的变电站继电保护二次回路隐藏故障状态在线检测方法。构建二次回路状态检测构架,利用电子式互感器获取二次设备的工作状态集信息,并分析其可靠性。采用循环神经网络构建二次回路隐藏故障状态在线检测模型,循环神经网络在处理时间序列数据方面具有显著优势。基于此,确定故障状态在线检测模型初始权重值,更新模型训练的学习因子,输出二次回路隐藏故障状态。实验结果表明,所提算法应用下的二次回路隐藏故障状态检测得出F1分数均在0.981左右,且该方法的收敛速度最快,说明研究提出的隐藏故障状态检测方法效率较高。

英文摘要:

The secondary circuit involves a large number of electrical parameters and signals, which may contain noise and have complex correlations with each other. Faults are often difficult to directly observe and their temporal characteristics are difficult to extract, making hidden fault detection difficult. To this end, a method for online detection of hidden fault states in the secondary circuit of substation relay protection based on recurrent neural networks is proposed. Construct a secondary circuit state detection framework, use electronic transformers to obtain the working state set information of secondary equipment, and analyze its reliability. Using recurrent neural networks to construct an online detection model for hidden fault states in secondary circuits, recurrent neural networks have significant advantages in processing time series data. Based on this, determine the initial weight value of the online fault state detection model, update the learning factor of the model training, and output the hidden fault state of the secondary circuit. The experimental results show that the F1 scores of the secondary circuit hidden fault state detection under the proposed algorithm are all around 0.981, and the convergence speed of this method is the fastest, indicating that the proposed hidden fault state detection method is highly efficient.

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