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Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
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Interpretable Detection of Partial Discharge in Power Lines with Deep Learning.

Gabriel Michau1, Chi-Ching Hsu1, Olga Fink1

  • 1Swiss Federal Institute of Technology, ETH Zürich, 8093 Zürich, Switzerland.

Sensors (Basel, Switzerland)
|April 3, 2021
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Summary

This study introduces a new deep learning framework for detecting partial discharge (PD) in power systems. The method enhances accuracy and interpretability, overcoming limitations of traditional PD detection techniques.

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fault detectionpartial dischargespower distribution linestemporal CNN

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Power Systems

Background:

  • Partial discharge (PD) is a critical indicator of faults in power systems, potentially leading to significant failures and outages.
  • Traditional PD detection methods rely on manual feature extraction, which are susceptible to noise and overlapping signals, reducing detection accuracy.
  • Developing robust and interpretable PD detection methods is crucial for maintaining power system reliability.

Purpose of the Study:

  • To propose a novel end-to-end framework for robust partial discharge detection using convolutional neural networks.
  • To eliminate the need for manual feature extraction, thereby improving detection performance in noisy environments.
  • To introduce a pulse activation map for enhanced interpretability of PD detection results.

Main Methods:

  • Development of a novel end-to-end framework utilizing convolutional neural networks (CNNs) for PD detection.
  • Implementation of a pulse activation map to visualize and identify PD-causing pulses, enhancing model interpretability.
  • Evaluation of the framework's performance on a public dataset for detecting damaged power lines.

Main Results:

  • The proposed CNN-based framework demonstrates robust partial discharge detection without manual feature engineering.
  • The pulse activation map provides valuable insights into the detection process, aiding domain experts in identifying critical PD events.
  • Performance evaluation on a public dataset confirms the framework's effectiveness, with ablation studies validating the contribution of each component.

Conclusions:

  • The novel end-to-end CNN framework offers a significant advancement in partial discharge detection, improving robustness and accuracy.
  • The integrated pulse activation map enhances the interpretability of PD detection, bridging the gap between AI models and expert knowledge.
  • This approach holds promise for more reliable and efficient monitoring of power system health.