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• W4386706839 endingPage "109838" @default.
• W4386706839 startingPage "109838" @default.
• W4386706839 abstract "The cable termination is an important part of the energy transmission of high-speed trains and is also a weak link in the insulation. It is important to determine the insulation status of the cable termination by the state of the partial discharge signal, but the partial discharge signal in the field test circuit is mixed with a large amount of external corona interference, which affects the detection accuracy. This paper proposes a wavelet transform and convolutional neural networks (WT-CNN) based classification model for the accurate and fast identification of corona disturbances in the partial discharge signals of cable termination for high-speed train. The method uses wavelet transform noise reduction and adaptive moment estimation (Adam) as the optimizer. The effect of WT on the classification effect of CNN is analyzed, and it is shown that the method can still achieve over 94% accuracy with small training samples." @default.
• W4386706839 created "2023-09-14" @default.
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• W4386706839 date "2023-12-01" @default.
• W4386706839 modified "2023-10-18" @default.
• W4386706839 title "Accurate identification partial discharge of cable termination for high-speed trains based on wavelet transform and convolutional neural network" @default.
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• W4386706839 doi "https://doi.org/10.1016/j.epsr.2023.109838" @default.
• W4386706839 hasPublicationYear "2023" @default.
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