Subdomain Adaptation Capsule Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Abstract

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.

Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

“…Mao et al [12] introduced domain-adversarial into prediction of remaining lifetimes under unknown working conditions and achieved reliable prediction. Wu et al [13] proposed adversarial subdomain adaptation for PD diagnosis, which reduced the distribution difference from the levels of domain and category. Wang et al [14] used domain-invariant LSTM for GIS PD location, which solved the sampling problem that restricted the development of the model.…”