A novel 1DCNN and domain adversarial transfer strategy for small sample GIS partial discharge pattern recognition

Recently, convolutional neural networks (CNNs) have made certain achievements in gas-insulated switchgear (GIS) partial discharge (PD) pattern recognition. However, these methods rely on the availability of massive PD samples and how to apply the CNN constructed in the laboratory to the field GIS PD pattern recognition has become an urgent problem. To solve these problems, we propose a small sample GIS PD pattern recognition using one-dimensional CNN (1DCNN) and domain adversarial transfer learning (DATL). First, a novel 1DCNN is constructed to achieve high-accuracy classification using unbalanced samples, where the problem of traditional two-dimensional CNN's dependence on sample size is solved. Second, DATL is used to realize on-site GIS PD pattern recognition using small samples containing some unlabeled samples. In the domain adversarial training, two domain classifiers are introduced to align the domain of the decision boundary, which achieves a suitable features migration and accurate classification of target domains. Through the construction of multiple experiments, we verified that the proposed method achieves 98.67% and >92% recognition accuracy in the source domain and target domain, respectively. Compared with the existing methods, the proposed method can realize satisfactory pattern recognition, which can provide strong support for the subsequent pattern recognition of GIS PD.