Characterization and Identification of Electrical Tree Growth Stages Inside High-Voltage Cable Insulation

Formation of microcavities takes place inside high-voltage cross-linked polyethylene (XLPE) cable insulation due to severe electrical and thermal aging, which further act as sources of void discharges and lead to the development of dendritic carbonyl channels, commonly referred to as electrical trees. Accompanied by intermittent partial discharge (PD) activities, the electrical tree growth bridges the entire insulation over time and results in insulation breakdown, which exposes the cable to fatal safety incidents. Therefore, accurately identifying various stages of electrical tree growths is crucial to prevent such safety instances as well as ensure reliable and extended operation of the XLPE cable. To this end, the present study proposes an automated electrical tree growth monitoring scheme employing a customized 19-layer residual neural network (ResNet) model. For this purpose, microscopic images of different tree growth stages inside XLPE insulation samples were acquired at different temperatures (30 °C, 50 °C, and 70 °C) and augmented using a deep convolutional generative adversarial network (DCGAN). Furthermore, using fractal dimension ( ${d}_{f}$ ) of the tree structures, different electrical tree propagation levels were characterized. Finally, different tree growth stages were identified using the proposed 19-layer ResNet model. This approach yielded satisfactorily high recognition accuracies irrespective of varying temperatures, engaging a significantly reduced computational time compared to classical convolutional neural network (CNN) models. This infers its potential application for automated health monitoring of XLPE cable.