Abstract：In recent years, the artificial intelligence diagnosis method driven by massive data has made a certain breakthrough in the diagnosis of gas-insulated switchgear (GIS) insulation defects. However, these methods built with massive laboratory data are difficult to deploy to complex working conditions and small sample conditions on site, which leads to difficulties in field application of existing diagnostic methods. In order to solve the problem of data scarcity that restricts the application of traditional diagnostic methods in the field and the problem of difficult field application of existing diagnostic models, this paper proposes a novel domain adversarial transfer convolutional neural network for small samples GIS insulation defect diagnosis. First, the convolutional neural network (CNN) constructed with automatic optimization learns transferable features from defective samples. The automatic optimization construction method reduces human intervention in the network construction process, and effectively improves the network accuracy and other aspects of performance. Then, domain adversarial transfer learning is introduced to realize the migration of the trained model under massive data (source domain) to small sample complex working conditions (target domain) to realize the reliable diagnosis under complex small samples. Through the adversarial training, the class boundary representation feature and the domain space representation feature are learned to realize the transfer of diagnostic knowledge. And two domain classifiers are introduced to align the domain space of the decision boundary, and a more suitable feature matching is realized. The experimental verification was carried out on the laboratory and on-site GIS, which verified that the proposed method achieved 99.35% and 90.35% diagnostic accuracy in the target domains, respectively. The results show that this method can effectively learn transferable features, and achieve high-precision and robust diagnosis of insulation defects in small samples of GIS.
王艳新, 闫静, 王建华, 耿英三, 刘志远. 基于域对抗迁移卷积神经网络的小样本GIS绝缘缺陷智能诊断方法[J]. 电工技术学报, 2022, 37(9): 2150-2160.
Wang Yanxin, Yan Jing, Wang Jianhua, Geng Yingsan, Liu Zhiyuan. Intelligent Diagnosis for GIS with Small Samples Using a Novel Adversarial Transfer Learning in Convolutional Neural Network. Transactions of China Electrotechnical Society, 2022, 37(9): 2150-2160.
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