高电压技术2025，Vol.51Issue(2)：753-765,13.DOI:10.13336/j.1003-6520.hve.20232204
基于多模态组合振动图像与堆叠稀疏自编码器的GIS设备机械缺陷诊断方法
Mechanical Defect Diagnosis Method of GIS Equipment Based on Multi-modal Combined Vibration Image and Stacked Sparse Autoencoder
摘要
Abstract
The mechanical defect of gas insulated metal enclosed switchgear(GIS)equipment has become an important hidden danger of power grid security.Aiming at the insufficient accuracy of existing defect diagnosis methods due to lim-ited feature information,in combination with deep learning theory,we proposed a GIS mechanical defect diagnosis method based on multi-modal combined vibration images and stacked sparse autoencoder.Firstly,the modal spectrum component of GIS original vibration signal was obtained by using variational mode decomposition algorithm,and the multi-modal combined vibration information image was constructed.Then,the support vector machine algorithm was used to construct a load classification model,and a double-layer stacked sparse autoencoder(DC-SSAE)was proposed to establish mechanical defect identification and severity assessment model under a large range current.Finally,based on the 550 kV GIS equipment mechanical defect test platform,vibration simulation tests under different currents were carried out to verify the effectiveness of the method.The results show that the feature representation of multi-modal combined vibration image is better than the traditional image,and the diagnostic model can fully mine the image information,over-coming the subjectivity of the traditional machine learning algorithm feature selection.The DC-SSAE model combined with load classification and defect matching can effectively diagnose GIS mechanical defects,and the overall accuracy of defect identification and severity evaluation is 99.38%and 99.44%,respectively.The method proposed in this paper has a good defect diagnosis effect,which can provide strong technical support for the safe and stable operation of GIS.关键词
GIS设备/机械缺陷/深度学习/故障诊断/自编码器Key words
GIS equipment/mechanical defects/deep learning/fault diagnosis/autoencoder引用本文复制引用
李滢,郝建,丁屹林,李旭,刘清松,钟尧..基于多模态组合振动图像与堆叠稀疏自编码器的GIS设备机械缺陷诊断方法[J].高电压技术,2025,51(2):753-765,13.基金项目
国家重点研发计划(2022YFB2403700) （2022YFB2403700）
重庆市自然科学基金(CSTB2022NSCQ-MSX1247).Project supported by National Key R&D Program of China(2022YFB2403700),Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX1247). （CSTB2022NSCQ-MSX1247）
