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Power transformer winding fault diagnosis method based on GSMallat-NIN-CNN network

ActiveCN111751763AProblems with suppression strength varying with distanceImprove integrityImage enhancementImage analysisAlgorithmGray level
The invention discloses a power transformer winding fault diagnosis method based on a GSMallat-NIN-CNN network, and the method comprises the steps: carrying out the measurement of a vibration condition of a transformer winding through a multi-channel sensor, and obtaining multi-source vibration data of a transformer; converting the multi-source vibration data obtained by measurement into grayscaleimages by using GST gray level transformation; decomposing each grayscale image into a high-frequency component sub-image and a low-frequency component sub-image layer by layer by adopting a Mallat algorithm, and performing image fusion on a high-frequency component sub-image and a low-frequency component sub-image; reconstructing the fused grayscale images, and encoding the vibration grayscale images according to the fault state of the transformer winding; establishing a transformer fault diagnosis model based on a GSMallat-NIN-CNN network; randomly initializing network parameters, dividinga training set and a test set, and training and optimizing the network through the training set; and storing the trained network, and testing the network through the test set. According to the method,t noise in a multi-source signal is effectively suppressed, the integrity of feature information is improved, the calculated amount is reduced, and the fault diagnosis accuracy is improved.
Owner:WUHAN UNIV
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