Abstract: Aiming at the problems that high voltage direct current（HVDC）transmission line protection and

fault location are vulnerable to lightning interference，and the traditional lightning interference identification

methods of transmission line based on time-domain and frequency-domain features exist the problems of difficult

threshold setting and poor noise robustness，a deep learning method was proposed to extract the characteristics of

lightning interference and short-circuit traveling wave and classify automatically. After phase mode decoupling and

wavelet packet decomposition，the current and voltage traveling wave components were input into the onedimensional convolutional block attention module convolutional neural network（CBAM-CNN）classification

model as different channels. Through simulation and example analysis，it is verified that the proposed model shows

higher recognition accuracy than the traditional methods，and the CBAM can effectively improve the noise

robustness of CNN classification model. At the same time，it is verified that the combination of four-layer wavelet

packet decomposition and the proposed CBAM-CNN model has the best performance.

      Key words: convolutional neural network（CNN）；convolutional block attention module（CBAM）；wavelet

packet decomposition（WPD）；DC transmission line；lightning interference；jonit time-frequency analysis（JTFA）