A Domain Adaptive Deep Transfer Learning Method for Gas-Insulated Switchgear Partial Discharge Diagnosis

Abstract

Intelligent fault diagnosis methods, especially convolutional neural network (CNN), have made significant progress in gas-insulated switchgear (GIS) partial discharge (PD) diagnosis, which are attributable to two reasons: 1) the training and testing samples come from the identical distribution; 2) there are massive labeled data with PD information. However, owing to the specific operating conditions of GIS, collecting massive samples from the same distribution is difficult in field conditions. With the purpose of resolving the data dilemma of conventional diagnosis methods in the field, we propose a domain adaptive deep transfer learning (DADTL) CNN for small samples GIS PD diagnosis. First, we adopted a CNN to automatically extract transferable features from PD samples. Then, a multilayer DADTL is developed to reduce the marginal and conditional distribution of learned transferable features, and Sliced Wasserstein distance (SWD) is employed as a penalty to reduce the negative migration. The experimental verification was performed on experimental and on-site GIS PD datasets. The results show that DADTL CNN can effectively achieve a high-accuracy diagnosis of GIS PD for small samples. Compared with other methods, the DADTL CNN can achieve more accurate and robust GIS PD diagnosis for on-site small samples.