电力系统保护与控制2026，Vol.54Issue(9)：89-101,13.DOI:10.19783/j.cnki.pspc.251078
基于生成对抗网络小样本扩展与多模态特征深度融合的变压器绕组故障诊断方法
Transformer winding fault diagnosis method based on small sample extension of generative adversarial network and deep multimodal feature fusion
摘要
Abstract
To address the scarcity of fault samples and dispersed multimodal features in transformer winding fault diagnosis,this paper proposes a diagnostic method combining a Wasserstein conditional generative adversarial network with gradient penalty(WCGAN-GP)and deep multimodal feature fusion.First,WCGAN-GP is employed to generate vibration and ultrasonic fault samples,alleviating the problems of small sample size and class imbalance.A dynamic time warping(DTW)-based 1-nearest neighbor(1NN)is introduced to quantitatively evaluate the quality of the generated samples.Then,differentiated feature extraction is applied for different modalities.Leakage flux signals are fed into a one-dimensional improved residual network to extract local dynamic features.Vibration and ultrasonic signals are transformed into two-dimensional feature maps and input in parallel into a two-dimensional improved dense network to extract global sequence relationships and time-frequency features.Finally,a layer-wise cross-modal interaction(LCMI)mechanism is designed to achieve deep fusion of the three modalities.Experiments show that the method effectively improves diagnostic performance under small-sample conditions,providing a reliable solution for transformer winding fault diagnosis.关键词
变压器绕组故障诊断/多模态融合/WCGAN-GP/残差网络/稠密连接网络/分层跨模态交互机制Key words
transformer winding fault diagnosis/multimodal fusion/WCGAN-GP/residual network/dense convolutional networks/layer-wise cross-modal interaction mechanism引用本文复制引用
邓祥力,沈宇卿,曾平,鲍伟,周德生,熊小伏..基于生成对抗网络小样本扩展与多模态特征深度融合的变压器绕组故障诊断方法[J].电力系统保护与控制,2026,54(9):89-101,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.52277079). 国家自然科学基金项目资助(52277079) （No.52277079）
国家电网有限公司科技项目资助(520940240037) （520940240037）
