Identification of XLPE cable insulation defects based on deep learning
Tao Zhou, Xiaozhong Zhu, Haifei Yang, Xuyang Yan, Xuejun Jin, Qingzhu Wan
Abstract
The insulation aging of cross-linked polyethylene (XLPE) cables is the main reason for the reduction in cable life. There is currently a lack of rapid and effective methods for detecting cable insulation defects in power-related sectors. To this end, this paper presents a method for identifying insulation defects in XLPE cables based on deep learning algorithms. First, the principle of the harmonic method for detecting cable insulation defects is introduced. Second, the ANSYS software is used to simulate the cable insulation layer containing bubbles, protrusions, and water tree defects, and the effects of each type of defect on the magnetic field strength and eddy loss current of the cable insulation layer are analyzed. Then, a total of 10 characteristic quantities of the total harmonic content and 2nd to 10th harmonic currents are constructed to establish a database of cable insulation defects. Finally, the deep learning algorithm, long short-term memory (LSTM), is used to accurately identify the types of insulation defects in cables. The results indicate that the LSTM algorithm can effectively diagnose and identify insulation defects in cables with an accuracy of 95.83%.