Intelligent Diagnosis of GIS Partial Discharge via Stacked Autoencoder and Transfer Learning

Accurate and reliable fault analysis is essential to improve the performance of gas-insulated switchgear (GIS) and ensure the safe and stable operation of the power system. Advances in the perception and measurement technology of power primary equipment have increased the scale of GIS operation and fault data. Driven by massive samples, deep learning has brought new opportunities for GIS fault identification and intelligence diagnosis. As an unsupervised learning method, stacked autoencoder (SAE) can automatically extract representative expressions and in-depth features from massive unlabeled data. Consequently, it can effectively alleviate the problems of incomplete data category labels and limited data samples. Meanwhile, transfer learning can avoid the need to train the model from scratch under the new dataset. For this reason, this paper combines SAE with transfer learning to realize partial discharge (PD) pattern recognition of GIS in order to solve the problem of complex and variable and large randomness of the on partial discharge samples. Unsupervised greedy layer-wise pre-training and supervised fine-tuning are leveraged to train the SAE model. The experimental data was generated using four types of PD in the laboratory. The optimal SAE structure is trained under the source domain, and the GIS PD pattern recognition under the target domain is realized through transfer learning. Experimental results show that the proposed method has stronger robustness and generalization while improving the accuracy of partial discharge pattern recognition of GIS.