基于LMD和LSTM的盆式绝缘子典型缺陷局部放电模式识别方法

Partial Discharge Pattern Recognition Method of Typical Defects in Basin Insulator Based on LMD and LSTM

盆式绝缘子是气体绝缘组合电器(gas insulated switchgear,GIS)中的重要绝缘部件,准确地识别其不同缺陷的局部放电信号对保障GIS长时间安全稳定运行具有重要意义。本文提出一种基于局部均值分解(local mean decomposition,LMD)和长短期记忆神经网络(long short-term memory,LSTM)的盆式绝缘子局部放电分类识别方法。首先使用成对高斯白噪声辅助的LMD方法对局部放电信号进行分解,再对分解得到的分量进行分割,提取每个片段的能量占比、Renyi熵和赫斯特指数形成特征矩阵,最后将特征矩阵输入LSTM中进行训练和分类识别。在实验室中建立了模拟实际工况的盆式绝缘子局部放电实验平台,采集了4种不同缺陷的局部放电信号进行分析处理,结果表明:所提方法能够有效识别盆式绝缘子不同缺陷的局部放电信号,经LMD分解后提取的特征参数能有效表征局放信号不同频带内的特性,识别正确率明显高于不经过LMD分解直接进行特征提取的情况。

Basin insulator is an important insulation component in gas insulated switchgear(GIS),and the accurate identification of its different defective partial discharge(PD)signals is of great significance to ensure the long-term safe and stable operation of GIS.In this paper,a classification and identification method for PD of basin insulators based on local mean decomposition(LMD)and long short-term memory(LSTM)neural network is proposed.Firstly,the PD signal is decomposed using the LMD method assisted by paired Gaussian white noise,and then the components obtained from the decomposition are segmented to extract the energy share,Renyi entropy and Hurst index of each segment to form feature matrices,and finally the feature matrices are sent to LSTM for training and classification.A basin insulator PD experimental platform is established in the laboratory to simulate the actual working conditions,and the PD signals of four different defects are collected for analysis and processing.The results show that the proposed method can effectively identify PD signals of different defects of basin insulators,and the feature parameters extracted by LMD decomposition can effectively characterize the characteristics of the PD signals in different frequency bands,and the recognition accuracy is significantly higher than that without LMD decomposition.