Journal of Guangxi Normal University(Natural Science Edition) ›› 2023, Vol. 41 ›› Issue (4): 74-83.doi: 10.16088/j.issn.1001-6600.2022083101

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Research on Identification of Lightning Overvoltage in Transmission Line by Improved Residual Network

JIANG Yibo, LIU Huijia*, WU Tian   

  1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang Hubei 443002, China
  • Received:2022-08-31 Revised:2022-11-06 Online:2023-07-25 Published:2023-09-06

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Abstract: To further improve the accuracy of over-voltage type identification during the operation and maintenance of high-voltage transmission lines, this paper proposes an improved Residual Network (ResNet) method for lightning over-voltage identification of transmission lines. The improvement of the traditional ResNet-50 network includes the following three aspects. Firstly, the 7×7 convolution kernel is replaced by three 3 × 3 convolution kernels to improve the feature extraction ability of the network. Secondly, the ReLU activation function of the traditional ResNet-50 network is replaced by the ReLU-Softplus activation function to improve the convergence speed of the network. Then, adjust the ResNet-50 network structure to enhance the role of batch standardization. Finally, a lightning overvoltage identification model of transmission lines is constructed by combining transfer learning and improved residual network. The experimental results show that the improved residual network converges in the 70th step and the recognition accuracy is 97.25%. The recognition accuracy and convergence speed are better than other models.

Key words: lightning overvoltage, deep learning, transfer learning, residual network, ResNet-50 network

CLC Number:  TM863; TP183
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