Discrimination of Single- and Multi-Source Corona Discharges using Deep Residual Network

Partial discharge (PD) is a major aging factor in insulation systems. The first step toward ensuring the health of insulation systems is the identification of PD sources. This would enable the system operator to take proper actions before the occurrence of the final breakdown. This fundamental study targets building and training a deep residual neural network (ResNet) model to detect and discriminate single and multiple sources of corona discharge that are acting simultaneously. The input of the ResNet model is the actual PD measurements under atmospheric pressure for seven electrode configurations: four single-source and three double-source configurations which expands from weakly nonuniform to extremely nonuniform electric field distribution. In this study, the measurement data are converted into standardized phase-resolved PD (PRPD) images that would be further used as input to the optimized ResNet model. The validation of the 62-layer deep neural network is evaluated through 30% randomly chosen images from the original dataset.