Application of multi‐source information fusion based on D‐S evidence theory in insulation defect identification of DC XLPE cable

Abstract

Abstract During the installation and laying process of cross‐linked polyethylene (XLPE) cable, it is difficult to avoid the influence of external harsh environment, which leads to insulation deterioration and failure. The partial discharge and leakage current play an important role in formation mechanism, characteristic law and identification technology of DC XLPE cable defect. However, the defect identification accuracy of single source data is limited. The correlation between partial discharge and leakage current of typical defect is analysed here. Based on the shape characteristic parameters of typical DC partial discharge spectrum and the energy characteristic parameters of leakage current wavelet decomposition node, a weighted Dempster–Shafer (D‐S) evidence theory is used to fuse and identify different information sources. The results show that the classification rate of the proposed method can reach 88%, which can effectively identify insulation defects.

Keywords

• inference mechanisms
• leakage currents
• partial discharge measurement
• power cable insulation
• uncertainty handling
• wavelet transforms