Abstract：Traction network overvoltage affects the normal operation of electrified railways. Identification of traction network overvoltage is helpful to improve the reliability of traction power supply system. In view of the nonlinearity and instability of traction network overvoltage, the short-time Fourier transform is used to convert the time-domain waveform of overvoltage into two-dimensional time-frequency diagram. Fast identification of ferromagnetic resonance overvoltage is realized by feature extraction and threshold setting. Then the self-learning ability of convolutional neural network is used to analyze the deep relationship between the time-frequency diagram characteristics and the overvoltage of traction network. The convolutional neural network realizes the identification of into/out neutral-section overvoltage, vacuum circuit breaker overvoltage and high-frequency resonance overvoltage. The test result shows that the accuracy of this method is over 90%.
贾君宜, 吴命利, 宋可荐, 王琪. 基于短时傅里叶变换和深度学习的牵引网过电压辨识[J]. 电气技术, 2021, 22(10): 1-10.
JIA Junyi, WU Mingli, SONG Kejian, WANG Qi. Traction network overvoltage identification based on short time Fourier transform and deep learning. Electrical Engineering, 2021, 22(10): 1-10.
2021, Vol. 22 
