Image Visibility Patch-Aided Partial Discharge Recognition Framework for Identifying Defects in XLPE Cables

Abstract

Partial discharge (PD) is a critical degradation phenomenon in cross-linked polyethylene (XLPE)-insulated polymeric power cables, which is responsible for premature failure if left unattended. Therefore, accurately identifying PD defects is essential to prevent such incidents in the XLPE cable. This study proposes a novel image visibility graph (IVG) theory-aided phase-resolved PD (PRPD) pattern analysis and recognition framework employing an optimally tuned bi-directional long short-term memory (bi-LSTM) classifier for automated PD detection. To this end, several PD defects have been synthetically emulated inside an 11-kV XLPE cable, and the PD signals corresponding to each type of defect are measured using an HFCT sensor. From the obtained HFCT data, the PRPD patterns were generated, which were converted into connected graphs using IVG. Moreover, image visibility patches (VPs) were computed from the graph-converted PRPD plots to quantify the intricate pixel-level changes due to altering discharge patterns. Following that, the frequency of occurrences (FOCs) of the unique visibility codes was computed from the extracted VPs. The visibility features were further employed to train the bi-LSTM classifier for PD defect identification, which yielded high accuracy. Ablation studies with classical convolutional neural network (CNN) models and comparison with previously reported state-of-the-art methods also revealed superior efficiency of the proposed PD detection methodology, suggesting its potential application for automated health monitoring of XLPE cable insulation.