An Internal Overvoltage Identification Method for Distribution Network Based on Transfer Learning - Hao XU; Liqiang LIU; Chao LV (Zhongguo dianli)
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.