Wu Yanze, Yan Jing, Xu Zhuofan, Sui Guoqing, Qi Meirong, Geng Yingsan, Wang Jianhua
State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China.
Entropy (Basel). 2023 May 17;25(5):809. doi: 10.3390/e25050809.
Deep learning methods, especially convolutional neural networks (CNNs), have achieved good results in the partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) in the laboratory. However, the relationship of features ignored in CNNs and the heavy dependance on the amount of sample data make it difficult for the model developed in the laboratory to achieve high-precision, robust diagnosis of PD in the field. To solve these problems, a subdomain adaptation capsule network (SACN) is adopted for PD diagnosis in GIS. First, the feature information is effectively extracted by using a capsule network, which improves feature representation. Then, subdomain adaptation transfer learning is used to accomplish high diagnosis performance on the field data, which alleviates the confusion of different subdomains and matches the local distribution at the subdomain level. Experimental results demonstrate that the accuracy of the SACN in this study reaches 93.75% on the field data. The SACN has better performance than traditional deep learning methods, indicating that the SACN has potential application value in PD diagnosis of GIS.
深度学习方法，尤其是卷积神经网络（CNN），在实验室中对气体绝缘开关设备（GIS）的局部放电（PD）诊断方面取得了良好的效果。然而，CNN中忽略的特征关系以及对样本数据量的严重依赖，使得在实验室中开发的模型难以在现场实现对局部放电的高精度、稳健诊断。为了解决这些问题，采用了一种子域自适应胶囊网络（SACN）用于GIS中的局部放电诊断。首先，通过使用胶囊网络有效地提取特征信息，这提高了特征表示。然后，使用子域自适应迁移学习在现场数据上实现高诊断性能，这减轻了不同子域的混淆并在子域级别匹配局部分布。实验结果表明，本研究中的SACN在现场数据上的准确率达到93.75%。SACN比传统深度学习方法具有更好的性能，表明SACN在GIS局部放电诊断中具有潜在的应用价值。
