An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning

doi:10.11930/j.issn.1004-9649.202006274

Abstract

Abstract: As a measure for internal overvoltage identification of distribution network, the data driving method is limited in practical applications due to the small number of overvoltage samples. A transfer-learning-based deep convolutional neural network (D-CNN) algorithm is thus proposed to identify the internal overvoltage of distribution network. Firstly, 6 types of two-dimension time-frequency maps of 10 kV distribution network internal overvoltage are constructed by simulation and continuous wavelet transform (CWT). Then, the transfer-learning-based D-CNN network models are built using four network models, including Alexnet, Vgg-16, Googlenet and Resnet50. Finally, the two-dimension time-frequency maps are introduced into the transformed D-CNN for training. By comparing and analyzing the experimental results, it is found that the newly constructed VGG-16 network model has the highest identification accuracy, reaching 99.07%, which realizes the accurate classification of overvoltage faults in the case of scarce data.

Key words: internal overvoltage of distribution network, continuous wavelet transform, transfer learning, deep convolutional neural network, pattern recognition

XU Hao, LIU Liqiang, LV Chao. An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning[J]. Electric Power, 2021, 54(8): 52-59.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202006274

https://www.electricpower.com.cn/EN/Y2021/V54/I8/52

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