Partial discharge (PD) measurements are crucial for evaluating the condition of the insulation systems of medium-voltage (MV) cables and their accessories. However, identifying PD defect types from phase-resolved partial discharge (PRPD) patterns still largely relies on expert knowledge. In this paper, the authors critically evaluate lightweight deep neural network architectures for automated classification of insulation defects from PRPD patterns: YOLOv8n, the MobileNetV2–YOLO hybrid network, and a compact SqueezeNet-based model. PD measurements were performed in a controlled environment in a factory laboratory for MV power cables in order to better evaluate the capability of the investigated models. The results demonstrate that lightweight deep neural architectures can effectively classify PRPD patterns and be deployed in a real measurement environment. The proposed approach has been integrated with the OMICRON MPD Suite measurement system, enabling automated defect recognition and visualisation during routine testing of MV cable.

Grounding is one of the key elements ensuring the reliable operation of overhead lines. The purpose of this paper is to provide a comprehensive assessment of grounding using a 400 kV overhead line as an example. The paper presents the results of diagnostic grounding measurements of various types of 400 kV overhead line poles, performed according to technical documentation and taking into account local terrain characteristics. The paper examines a large number of overhead line poles in both uniform and varied terrains, taking into account soil conditions based on geotechnical documentation. Furthermore, the paper presents a simulation analysis of lightning current dissipation through the grounding of a selected pole, taking into account geotechnical soil investigations with respect to soil stratification. The final section of the paper includes guidelines on grounding and assessment of the technical condition of grounding systems, which may be useful to transmission and distribution network operators.

The correct classification of defects originating from partial discharges (PD) in medium-voltage (MV) switchgears with air insulation (AIS) remains a challenging research topic for scientists worldwide. In this article, the authors simulated four possible defects occurring in the power industry, including one that is a simultaneous combination of two commonly ones. In addition, the correctness of the algorithm was checked by adding a classification class without any fault. The measurement signals were recorded with TEV sensors. The effectiveness of various hy-brid-connected neural networks was tested and discussed: GoogleNet and SqueezeNet based on spectrograms, SAE with FNN, 2D-CNN with LSTM, and hybrid AE combined with CNN and LSTM. The highest effectiveness – approximately 97% – was demonstrated by the GoogleNet and SqueezeNet networks. The research results are expected to form the basis for the development of a universal and wireless capacitive sensor for monitoring the level of PD in switchgears.