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.

ENET centre CEET VSB Technical University of Ostrava Ostrava Czech Republic

Faculty of Electric Engineering and Computer Science VSB Technical University of Ostrava Ostrava Czech Republic

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Talaat, M., El-Shaarawy, Z. & Tayseer, M. An economic study concerning the cost reduction of the covered transmission conductors based on different optimization techniques.

Dabbak, S., Illias, H., Chin, A. B. & Tunio, M. A. Surface discharge characteristics on HDPE, LDPE and PP.

Pakonen, P.

Kabot, O., Fulneček, J., Mišák, S., Prokop, L. and Vaculík, J. Partial discharges pattern analysis of various covered conductors. In

Chiu, B., Roy, R. & Tran, T. Wildfire resiliency: California case for change.

Stone, G. C. Partial discharge diagnostics and electrical equipment insulation condition assessment.

Kaziz, S.

Leskinen, T. Finnish and slovene experience of covered conductor overhead lines. In

Hamacek, S. Problems of covered conductors running, (2012).

Misak, S., Fulnecek, J., Jezowicz, T., Vantuch, T. and Burianek, T. Usage of antenna for detection of tree falls on overhead lines with covered conductors.

Martinovic, T. and Fulnecek, J. Fast algorithm for contactless partial discharge detection on remote gateway device.

Klein, L.

Klein, L., Žmij, P. and Krömer, P. Partial discharge detection by edge computing.

Ieee standard definitions of terms relating to corona and field effects of overhead power lines.

Dehlinger, N. & Stone, G. Surface partial discharge in hydrogenerator stator windings: Causes, symptoms, and remedies.

Cheng, J., Xu, Y., Ding, D. & Liu, W. Investigation of sensitivity of the ultra-high frequency partial-discharge detection technology for micro-crack in epoxy insulator in gis.

Kumar, H., Shafiq, M., Kauhaniemi, K. and Elmusrati, M. A review on the classification of partial discharges in medium-voltage cables: Detection, feature extraction, artificial intelligence-based classification, and optimization techniques.

Fulnecek, J. & Misak, S. A simple method for tree fall detection on medium voltage overhead lines with covered conductors.

Kaggle. Vsb power line fault detection, (2019).

Klein, L. PubMed PMC

Kabot, O., Klein, L., Prokop, L. and Walendziuk, W. Enhanced fault type detection in covered conductors using a stacked ensemble and novel algorithm combination. PubMed PMC

Kabot, O., Klein, L., Prokop, L., Mišák, S. and Slanina, Z. Dataset for antenna-based detection of fault types in covered conductors for 22 kv voltage power lines. PubMed PMC

Ahmad, D., Wang, S., and Alam, M. Long short term memory based deep learning method for fault power line detection in a mv overhead lines with covered conductors. In

Vantuch, T., Prílepok, M., Fulneček, J., Hrbáč, R. and Mišák, S. Towards the text compression based feature extraction in high impedance fault detection.

Wang, Y., Chiang, H.-D. and Dong, N. Power-line partial discharge recognition with hilbert dash;huang transform features.

Elmasry, W. & Wadi, M. Enhanced anomaly-based fault detection system in electrical power grids.

Elmasry, W. & Wadi, M. Edla-efds: A novel ensemble deep learning approach for electrical fault detection systems.

Michau, G., Hsu, C. C. & Fink, O. Interpretable detection of partial discharge in power lines with deep learning. PubMed PMC

Liu, H.

Kabot, O., Fulneček, J., Mišák, S., Prokop, L. and Vaculík, J. Partial discharges pattern analysis of various covered conductors. In

Kabot, O., Klein, L., Prokop, L., Mišák, S. and Slanina, Z. Dataset for antenna-based detection of fault types in covered conductors for 22 kv voltage power lines, (2024). PubMed PMC

Uwiringiyimana, J. P.

Kabot, O., Klein, L., Slanina, Z. and Prokop, L. Partial vs. corona discharges in xlpe-covered conductors: High-resolution antenna dataset for ML applications. PubMed PMC

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