电机与控制应用2024，Vol.51Issue(1)：29-38,10.DOI:10.12177/emca.2023.162
基于格拉姆角场和深度残差网络的变压器绕组松动故障诊断模型
Transformer Winding Looseness Fault Diagnosis Model Based on GAF and Depth Residual Network
摘要
Abstract
Aiming at the problem that the feature quantity is difficult to select in the fault diagnosis of transformer winding looseness and relying on manual experience,a diagnosis method of transformer winding looseness based on automatic encoder noise reduction,gramian angle field(GAF)and depth residual network(ResNet)recognition is proposed.The method automatically learns effective fault features from GAF images without manually extracting the feature quantity.Firstly,the vibration signal is denoised through an automatic encoder to obtain a vibration signal with a higher signal-to-noise ratio.Then,the GAF method is used to convert the vibration signal into a two-dimensional image and generate an image dataset.Based on this,ResNet is trained to construct a network model suitable for classification and recognition of transformer winding looseness faults.Finally,a transformer winding looseness fault test platform is built to collect vibration signals of the winding under different looseness and experimental currents for analysis.The experimental results show that the proposed diagnosis method has an accuracy of over 95％in identifying transformer winding looseness,and can effectively identify the looseness phase and degree.It is suitable for identifying and diagnosing transformer winding looseness faults.关键词
变压器振动/绕组松动/降噪自动编码器/格拉姆角场(GAF)/深度残差网络Key words
transformer vibration/winding looseness/de-noise auto-encoder/gramian angular field(GAF)/depth residual network分类
信息技术与安全科学引用本文复制引用
肖雨松,马宏忠..基于格拉姆角场和深度残差网络的变压器绕组松动故障诊断模型[J].电机与控制应用,2024,51(1):29-38,10.基金项目
国家自然科学基金(51577050) （51577050）
国家电网江苏省电力有限公司重点科技项目(J2022047)National Natural Science Foundation of China(51577050) （J2022047）
Science and Technology Foundation of State Grid Jiangsu Electric Power Corporation(J2022047) （J2022047）
