Transformer Partial Discharge Pattern Recognition Based on RF and ResNet with Self-Attentional
Partial discharge of transformer is an important factor affecting the efficient and stable operation of power grid,and it is also an important content of transformer state detection.Accurate detection of partial discharge and identification of partial discharge pattern are the preconditions for timely fault detection and elimination of haz-ards.Aiming at the problems of low recognition rate of existing recognition methods,the paper presented a transformer partial discharge pattern recognition based on Random Forest(RF)and Residual Network(ResNet)with self-atten-tional.This method built a RF and self-attention ResNet model.Aiming at the problem of low classification perform-ance of residual network,this paper introduced RF as a classifier,which can improve the recognition rate and avoid network degradation.And it integrated the self-attention mechanism to optimize partial discharge features and accel-erate the model training.The experiment results show that the method can more accurately identify transformer partial discharge pattern,with 98.96%accuracy.And through the crossover experiments,in practical situations,the method can effectively identify transformer partial discharge pattern of various voltage levels in different environments.And it has good versatility.
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