A novel method for small and unbalanced sample pattern recognition of gas insulated switchgear partial discharge using an auxiliary classifier generative adversarial network

Convolutional neural networks (CNNs) have been widely used for gas-insulated switchgear (GIS) partial discharge (PD) pattern recognition due to their powerful feature extraction ability. However, there is commonly a scarcity of fault samples due to low insulation failure rate of GIS equipment, which degrades the diagnostic performance of these CNN networks when directly applied to small and unbalanced datasets. Therefore, we propose a novel auxiliary classifier generative adversarial network for GIS PD pattern recognition for small and unbalanced samples. First, we propose using synchrosqueezed wavelet transform to extract time-frequency characteristics of PD pulses and obtain a time-frequency image with high energy aggregation and time-frequency distribution rate. Then, we propose an improved generative adversarial network with an auxiliary classier and self-attention mechanism, which can generate high-quality PD samples for situations with few classes. Experiments show that our proposed method can reach 95.75% recognition accuracy for small datasets, which is the highest among several comparable methods. Furthermore, the proposed method has excellent and stable recognition performance for various unbalanced datasets.