Transformer Bushing Hydrophobicity Identification Method Based on Multi-Scale Features and Image Completion
This paper proposes a method of transformer bushing hydrophobicity identification based on multi-scale features and image completion,which can identify the bushing hydrophobicity timely and accurately,avoid electrical faults,and improve the reliability of transformer operation and the stability of power system.BSCB algorithm is used to complete the defective area in the transformer bushing image to obtain the complete image.LBP algorithm was used to extract the texture feature vector of the completed image,and Canny algorithm was used to segment the completed image to obtain the image of water drop and background segmentation.Four features closely related to casing hydrophobicity were extracted from the segmented image to describe casing hydrophobicity characteristics.A probabilistic neural network model is constructed,and the obtained texture feature vector and casing hydrophobicity feature quantity are input to the trained probabilistic neural network model.The output result is the final recognition result of transformer casing hydrophobicity.Experimental results show that this method can accurately identify the hydrophobicity level of the transformer bushings,and the identification effect is best when the smoothing factor in the probabilistic neural network model is set to 0.7.