Workmanship defect classification in medium voltage cable terminations with convolutional neural network

•CNN-based defect classification in cable terminations.•Elimination of complex feature extraction stages for defect discrimination using automated feature extractor.•Optimization of CNN architecture and training hyperparameters.•Higher accuracy performance of the optimized CNN architecture. This paper presents a method based on convolutional neural network (CNN) for classifying workmanship defects located in 36 kV cross-linked polyethylene (XLPE) cable terminations. The main contributions of the study are to differentiate the poor workmanship defects without a hand-crafted feature extraction process, and to propose a new input type for the partial discharge (PD) recognition algorithm. Experiments are carried out on two different datasets, each of which has five typical cable termination defects. A database comprised of 1200 phase-resolved partial discharge (PRPD) defect patterns are generated, and each PRPD fingerprint recorded for 30 s is converted into an RGB image for inputting them to the CNN. Three case studies are created to increase the robustness of the algorithm by using the two datasets. The algorithm hyperparameters are optimized to improve the performance of CNN. Finally, the proposed method is compared with the state-of-art CNN algorithms used in the literature. The results show that the proposed method is viable for determining the types of potential defects in the cable terminations.