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IEEE/ICACT20220141 Slide.14        [Big Slide]	Oral Presentation
	A partial discharge recognition method was introduced in this paper. Four kinds of PD signals were collected through a high-voltage partial discharge device and high-frequency current sensors. We use the wavelet transform to remove interference of the time-domain signal, and CNN was used to extract features and recognition. The accuracy of recognition is 91.8%. Compared with BP neural network[19] and OS-ELM algorithms, which the accuracy are 87.8% and 91.4% respectively, our methods get higher accuracy.

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