Partial discharge characteristics and defect type identification of typical defects in oil-pressboard insulation

In view of the problem of partial discharge and insulation deterioration caused by internal defects in the oil-paper insulation of oil-immersed transformers, an identification method is proposed based on partial discharge signals of three typical defective oil-pressboards and deep neural networks. According to the characteristics of the partial discharge spectrum signals from different types of the oil-paper insulation in oil-immersed transformers, the statistical parameters of the spectrum were determined as the characteristic quantities. The influence of different parameters of the deep neural network on the recognition effect was analyzed and compared and the optimal deep neural network structure was found. The partial discharge pattern recognition was carried out through the characteristic quantities of various partial discharge signals and deep neural networks. The research results show that statistical parameters can characterize the distribution characteristics of the partial discharge pattern signals. Optimizing the deep neural network can improve the convergence speed and accuracy of the model. The combination of the statistical parameters from the partial discharge map signal and the deep neural network can identify the partial discharge signals of the different internal defects of the oil-paper insulation in oil-immersed transformers.And the recognition result is higher than the K-neighbor method, support vector machine and back propagation neural network. © 2022, Harbin University of Science and Technology Publication. All right reserved.