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This study introduces a high-resolution dataset for distinguishing partial discharges (PD) and corona discharges in power systems. This resource aids in developing machine learning models for accurate fault diagnosis and improved infrastructure safety.

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Data Science

Background:

  • Accurate differentiation between partial discharges (PD) and corona discharges is vital for power system diagnostics.
  • Existing machine learning (ML) model development is hindered by a lack of specialized, high-fidelity datasets for PD and corona discharge classification.

Purpose of the Study:

  • To present a high-resolution dataset for PD and corona discharge classification.
  • To enable the development and benchmarking of ML models for power system diagnostics.
  • To support advancements in non-invasive fault identification and insulation risk mitigation.

Main Methods:

  • Acquisition of a high-resolution dataset (10^7 samples/20 ms) using a contactless dual-antenna system.
  • Controlled laboratory conditions simulating medium-voltage overhead distribution lines.
  • Collection of 100 labeled measurements per class across five discharge types and two background conditions over two days.

Main Results:

  • A novel, high-fidelity dataset with experimentally isolated signal types for PD and corona discharges.
  • Dataset enables benchmarking of ML models for PD-corona classification.
  • Facilitates research into lightweight models, synthetic data generation, and noise robustness.

Conclusions:

  • The presented dataset is a key resource for advancing ML-based diagnostics in power systems.
  • Enables development of accurate, non-invasive fault identification tools.
  • Contributes to enhanced diagnostic accuracy, reduced insulation risks, and improved power infrastructure safety.