贾俊青，等 ELECTRIC DRIVE 2022 202 JIA Junqing1，LÜ Chao1，LIU Dinghua1，XU Hao2 （1. Inner Mongolia Electric Power Research Institute，Huhhot 010020，Nei Monggol，China； 2. School of Electric Power，Inner Mongolia University of Technology，Huhhot 010321，Nei Monggol，China） Abstract: With the rapid development of artificial intelligence algorithms，convolutional neural networks with fewer layers have been used in distribution network overvoltage recognition. The deep-level network has a higher recognition rate，but requires a large number of data samples. At present，the amount of data in the existing data set is insufficient to meet the needs of deep-level network training. To this end，a method for establishing distribution network overvoltage data sets required for deep-level network training was proposed. Firstly，the electromagnetic transient simulation software EMTPworks was used to simulate 5 typical overvoltages of 10 kV distribution network and the corresponding JavaScript script was edited，and 16 272 pieces of data were generated
