Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications

Accurate differentiation between partial discharges (PD) and corona discharges in XLPE-covered conductors is crucial for power system diagnostics, yet remains limited by the lack of specialized, high-fidelity datasets for machine learning (ML) model development. This paper presents a high-resolution dataset (10⁷ samples per 20 ms) acquired using a contactless dual-antenna system under controlled laboratory conditions simulating medium-voltage overhead distribution lines. The dataset includes 100 labeled measurements per class across five discharge types (PD, corona, mixed states, and high-impedance variants) and two background conditions (with and without high voltage), collected over a two-day campaign. By providing experimentally isolated signal types, this resource enables the development and benchmarking of ML models specifically tailored to the PD-corona classification challenge. Key applications include lightweight classification models for edge devices, synthetic data generation to augment limited training sets, and investigations into noise robustness, real-time monitoring, and explainable diagnostics. Through a controlled yet realistic acquisition design, the dataset supports the creation of advanced ML-based tools for non-invasive fault identification-enhancing diagnostic accuracy, mitigating insulation risks, and improving safety in critical power infrastructure.