摘要
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 timefrequency distribution rate.Then,we propose an improved generative adversarial network with an auxiliary classier and self-attention mechanism,which can generate highquality 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.
