1.School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2.Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China

To further improve the fault recognition accuracy of on-load tap-changer (OLTC) of power transformer, this paper presents a fault recognition method of OLTC based on convolutional neural network (CNN) considering the texture features of recurrence plot for vibration signal during the switching process of OLTC. The distance mapping recurrence plot (DMRP) is constructed by using the phase point distance mapping according to the phase space distribution of vibration signals for OLTC. Then a CNN based OLTC fault recognition model is proposed by reasonably selecting the structure hyper-parameters of CNN, including the number of network layers and convolution kernel size, and reducing the convolution kernel dimensionality. The calculation results of vibration signals from a CM type OLTC under normal and typical mechanical fault conditions show that the DMRP can adaptively describe the point distribution of the vibration signals in phase space. The recognition model has excellent recognition performance on OLTC typical faults. Especially, the recognition rate of slight fault is improved by at least 10% compared with the existing methods.