信息与控制2025，Vol.54Issue(3)：442-452,11.DOI:10.13976/j.cnki.xk.2024.0962
基于图像化特征提取和双层特征优选的变压器故障诊断
Transformer Fault Diagnosis Based on Image Feature Extraction and Dual-layer Feature Selection
摘要
Abstract
Transformer fault diagnosis often lacks a unified criterion for feature selection,with redundant features reducing the diagnostic performance.To address this,we propose a novel transformer fault diagnosis method based on image feature extraction and dual-layer feature selection.Using dis-solved gas in transformer oil as the research object,we transform the concentration of dissolved gas into images using the Gramian Angular Field(GAF)method,followed by data equalization through image processing algorithms.Second,we transfer the feature parameters of the VGG16(Visual Geometric Group 16)algorithm to the GAF images to construct a fault diagnosis model that automates feature extraction.After comprehensively considering the importance score and correlation coefficient,we improve the random forest algorithm to filter important features and es-tablish a dual-layer feature preference model to enhance the fault diagnosis ability of power trans-formers.On this basis,the effectiveness of the proposed method is verified by four classifiers:lin-ear regression,support vector machine,multilayer perceptron,and stochastic gradient descent.The experimental results show that the visualization-based method extracts fault features more effec-tively than traditional approaches.After the dual-layer optimization of the features,the accuracy of transformer fault diagnosis is improved by 4.27％,11.2％,6.1％,and 10.97％,respectively,and the F1 value is improved by 4.53％,12.55％,6.08％and 11.1％respectively.The trans-former operation state is identified more accurately.关键词
变压器/故障诊断/格拉姆角场(GAF)/VGG16网络/双层特征优选Key words
transformer/fault diagnosis/Gramian angle field(GAF)/VGG16 network/dual-layer feature selection分类
动力与电气工程引用本文复制引用
王凌云,李冉,杨波,李婷宜,李振华..基于图像化特征提取和双层特征优选的变压器故障诊断[J].信息与控制,2025,54(3):442-452,11.基金项目
国家自然科学基金项目(52277012) （52277012）
