Transient earth voltage (TEV) measurements are widely used for partial discharge (PD) monitoring in medium-voltage equipment due to their non-intrusive nature and suitability for field deployment. However, TEV-based PD analytics remain challenging in practical environments because PD signatures often overlap with noise characteristics. Recent advances in deep learning enable the image-based analysis of phase-resolved partial discharge (PRPD) representations, offering improved robustness compared to conventional signal-based methods. This paper presents a structured experimental investigation of classification-based and localisation-based deep learning models for TEV PRPD analytics. A multi-column convolutional neural network (MCNN) is evaluated as a classification model, while YOLOv5 and YOLOv12, based on the You Only Look Once (YOLO) framework, are investigated as object detection frameworks capable of PD localisation. Experiments are conducted using both original and expanded datasets, with additional analysis on the impact of hyperparameter optimisation. The results show that dataset expansion and hyperparameter tuning improve classification and detection performance for several models. The MCNN achieves strong classification accuracy for PD/noise screening, while YOLOv5 demonstrates substantial improvement in localisation performance after optimisation. In contrast, YOLOv12 maintains stable detection performance under the evaluated training configurations. The results highlight the trade-offs between classification accuracy, localisation capability, and annotation complexity when selecting deep learning models for PD analytics. These findings provide practical insights into the deployment of deep learning techniques for TEV-based PD monitoring systems and highlight the complementary roles of classification and localisation frameworks in PRPD analysis.
Sim et al. (Sat,) studied this question.