基于T-MobileNet-L模型的GIS局部放电模式识别研究  

Research on pattern recognition of GIS partial discharge based on T-MobileNet-L model

作　　者：程江洲[1] 温静怡 鲍刚[1] 何艳 陈奕睿 Cheng Jiangzhou;Wen Jingyi;Bao Gang;He Yan;Chen Yirui(School of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院,宜昌443002

出　　处：《电子测量技术》2021年第20期22-28,共7页Electronic Measurement Technology

基　　金：国家自然科学基金(61876097)项目资助。

摘　　要：针对当前GIS局部放电模式智能识别过程中存在计算资源消耗大以及缺少真实标签数据的问题,利用激活函数为Leaky ReLU的MobileNet-V2模型,在减少模型参数量的同时提取大量的图像特征信息。并融合迁移学习对模型参数进行预训练,在减少网络对输入数据量需求的同时提高模型的识别准确性。结果表明,该模型的参数量可降至2.24×10^(6),并且对于干扰以及GIS局部放电模式识别的平均准确率分别达到95.8%和92.1%,与传统深度学习模型相比,该模型在显著降低计算复杂度的同时提升模式识别的准确率,对实际GIS设备进行有效、智能、轻量化运维检修具有一定的价值与意义。Aiming at the problems of large computing resource consumption and lack of real label data in the process of partial discharge mode intelligent identification in GIS.This paper uses the MobileNet-V2 model whose activation function is Leaky ReLU to extract a large amount of image feature information while reducing the amount of model parameters.It also integrates migration learning to pre-train the model parameters,which reduces the network′s need for input data and improves the recognition accuracy of the model.The results show that the parameter quantity of the model can be reduced to 2.24×10^(6),and the average accuracy of interference and partial discharge pattern recognition in GIS reaches 95.8%and 92.1%,respectively.Compared with the traditional deep learning model,this model can significantly reduce the computational complexity and improve the accuracy of pattern recognition,which has certain value and significance for effective,intelligent and lightweight operation and maintenance of actual GIS equipment.

关 键 词：气体绝缘组合电器 MobileNet-V2 迁移学习 故障诊断 智能运维 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TM855[自动化与计算机技术—计算机科学与技术]