基于多层融合振动数据分析的GIS设备机械缺陷诊断方法研究  

Research on the GIS Mechanical Defects Diagnosis Method Based on Multi-layer Fusion Vibration Data Analysis

作　　者：冯英[1] 李旭[2] 钟尧 郝建[2] 武建文[1] FENG Ying;LI Xu;ZHONG Yao;HAO Jian;WU Jianwen(School of Automation Science and Electrical Engineering,Beihang University,Haidian District,Beijing 100191,China;State Key Laboratory of Power Transmission Equipment&System Security and New Technology(Chongqing University),Shapingba District,Chongqing 400044,China)

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京市海淀区100191 [2]输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆市沙坪坝区400044

出　　处：《中国电机工程学报》2024年第14期5797-5809,I0031,共14页Proceedings of the CSEE

基　　金：国家重点研发计划项目(2022YFB2403700,2022YFB2403705);重庆市自然科学基金(CSTB2022NSCQ-MSX1247)。

摘　　要：气体绝缘金属封闭开关设备(gas insulated metal enclosed switchgear,GIS)机械缺陷是导致设备故障的重要因素,针对单测点、单证据机械缺陷诊断模型信息缺失和精度不足问题,该文提出一种多层融合振动数据分析的GIS设备机械缺陷诊断方法。首先,基于真型GIS设备振动模拟平台试验研究测点位置与缺陷类型对振动行为的影响特性;然后,联合统计分析、模态分解、尺度变换方法提出机械振动信号整体与局部信息关注的复合参数分析方法,引入主成分分析开展多测点振动信息的特征层融合降维;最后,提出改进放缩权重的Dempster-Shafer(DS)证据理论和Bagging投票机制的强/弱基学习器决策层融合机制,联合构建多层融合振动数据分析的GIS设备机械缺陷诊断模型。结果表明:不同类型机械缺陷信号的响应幅值、特征频点和畸变程度存在显著差异,复合特征参量大小及分散程度各不相同;同时,测点位置对缺陷信号的复合振动特征参量的表现形式及分布区间也具有一定影响;基于多层融合数据分析的诊断模型实现缺陷有效识别,辨识准确率为98.66%,相比单一分类器诊断效果提升5.83%。该文可为GIS设备机械缺陷诊断方法提供有价值的参考。The mechanical defects of gas insulated metal enclosed switchgear(GIS)equipment are the important factor leading to its failures.To solve the problems of missing information and insufficient accuracy in single measurement point and single evidence mechanical defect diagnosis model,this paper proposes a multi-layer fusion data analysis method for GIS mechanical vibration defects diagnosis.First,based on the real GIS vibration simulation platform,the influence of measurement point positions and defect types on vibration behavior are studied through experiments.Then,combined with statistic analysis,mode decomposition,and scale transformation methods,a composite parameter analysis method is proposed for the overall and local information of mechanical vibration signals,and principal component analysis(PCA)is introduced to perform feature layer fusion and dimensionality reduction of multiple measurement points vibration information.Finally,a strong and weak basis learner decision layer fusion mechanism is proposed based on the scaling weight Dempster-Shafer(DS)evidence theory and Bagging voting mechanism,and the GIS vibration defects diagnosis method based on multi-layer fusion data analysis is jointly constructed.Results show that the position of the measuring points and the types of defects have influence on the amplitude,frequency,and distribution range of vibration response.The distribution of composite vibration characteristic parameters shows significant differences.The diagnosis model based on multi-layer data fusion analysis has achieved an effective accuracy of 98.66%for GIS mechanical defects,which improves the diagnostic performance by 5.83%to single classifier.This paper can provide a valuable reference for the diagnosis method of GIS mechanical defects.

关 键 词：气体绝缘金属封闭开关(GIS)设备 机械缺陷 复合特征 多层融合 

分 类 号：TM595[电气工程—电器]