摘要
变压器内部故障诊断通常需要利用油中溶解气体进行分析,但这些信息提取、检测分析过程繁琐,实时性较差。因此文章中提出了一种仅需要电气量的变压器内部故障快速诊断方法,采用小波包分析提取短路电流和差动电流的频域故障特征,采用最大值体现零序电流的故障特征,采用信息融合技术将所得到的所有故障特征进行融合,并利用BP神经网络算法对变压器内部电气故障类型进行诊断。在MATLAB/Simulink平台建立来仿真模型并进行了算例分析,结果表明文章中所提出的变压器内部电气故障诊断方法具有高准确性和高可靠性的优点。
Transformer internal fault diagnosis usually requires analysis of dissolved gas in oil,but the information extraction,detection and analysis process is cumbersome,and the real-time performance is poor.Therefore,this paper proposes a fast diagnosis method of transformer internal fault which only needs electrical quantity information.The frequency domain fault characteristics of short-circuit current and differential current are extracted by wavelet packet analysis.The fault characteristics of zero-sequence current are represented by the maximum value.The information fusion technology is used to fuse all the fault features,and the BP neural network algorithm is used to diagnose the internal electrical fault types of transformer.The simulation model is established on Matlab/Simulink platform and the example analysis is carried out,the results show that the proposed internal electrical fault diagnosis method of transformer has high accuracy and high reliability.
作者
李婷
肖京
刘赟
彭平
钟永恒
乐健
Li Ting;Xiao Jing;Liu Yun;Peng Ping;Zhong Yongheng;Le Jian(Electric Power Research Institute,State Grid Hunan Electric Power Co.,Ltd.,Changsha 410007,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China)
出处
《电测与仪表》
北大核心
2023年第11期194-200,共7页
Electrical Measurement & Instrumentation
基金
国网湖南省电力有限公司科技项目(5216A520000S)。
关键词
电力变压器
内部故障
信息融合技术
神经网络
故障诊断
power transformer
internal fault
information fusion technology
neural network
fault diagnosis
