电力系统保护与控制2024，Vol.52Issue(23)：1-9,9.DOI:10.19783/j.cnki.pspc.240153
基于混合特征选择和IOMA-CNN的变压器故障诊断
Transformer fault diagnosis based on hybrid feature selection and IOMA-CNN
摘要
Abstract
There are problems of insufficient types of dissolved gas fault features in transformer oil and a low accuracy of diagnosis.Thus a hybrid feature selection method is proposed.The improved optical microscope algorithm(IOMA)is used to optimize convolutional neural networks(CNN)to realize transformer fault diagnosis.First,a 30 dimensional transformer fault candidate feature set is constructed based on the correlation ratio method,and the hybrid feature selection method is used to determine the feature dimension of the input set through two feature selections.Secondly,a Tent chaotic mapping,adaptive t-distribution mutation and dynamic selection strategy are introduced to improve the optical microscope algorithm(OMA)and enhance its optimization performance.Then,the learning rate,the size and number of convolution kernels of the CNN model are optimized using the IOMA algorithm.Finally,the IOMA-CNN transformer fault diagnosis model is constructed and its performance is evaluated by numerical example analysis.Experiments show that the fault diagnosis accuracy of the proposed method is 98.5%.Compared with the conventional feature selection method,the fault diagnosis accuracy can be effectively improved by using the input features selected by the hybrid feature selection method.Compared with other optimized diagnosis models,IOMA-CNN has higher accuracy and better stability.关键词
变压器/故障诊断/混合特征选择/光学显微镜优化算法/卷积神经网络Key words
transformer/fault diagnosis/hybrid feature selection/optical microscope optimization algorithm/convolutional neural network引用本文复制引用
闵永智,令世文,王果..基于混合特征选择和IOMA-CNN的变压器故障诊断[J].电力系统保护与控制,2024,52(23):1-9,9.基金项目
This work is supported by the National Natural Science Foundation of China(No.62066024). 国家自然科学基金项目资助(62066024) （No.62066024）
