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This study introduces a novel method for precise arc fault detection and localization in power systems. By using a multi-square microstrip antenna and advanced signal processing, it accurately pinpoints arcing sources, enhancing electrical safety.

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Area of Science:

  • Electrical Engineering
  • Power Systems Safety
  • Electromagnetic Interference

Background:

  • Existing arc fault detection methods face challenges in differentiating normal and abnormal conditions, especially in confined spaces.
  • Accurate localization of arcing faults is crucial for power system safety and maintenance.
  • Electromagnetic radiation detection using strategically placed antennas offers a viable solution for monitoring in limited areas.

Purpose of the Study:

  • To propose and evaluate an integrated experimental approach for arc fault detection and localization.
  • To compare the effectiveness of Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT) for signal processing in arc fault detection.
  • To assess the accuracy of Time of Arrival (ToA) based localization methods (Least Square, Gauss-Jordan Elimination) using experimental data.

Main Methods:

  • Utilized a compact multi-square microstrip antenna for detecting electromagnetic radiation from arcing faults.
  • Applied Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT) to process captured signals and isolate arc signatures.
  • Employed Time of Arrival (ToA) measurements combined with Least Square and Gauss-Jordan Elimination algorithms for arc source localization.

Main Results:

  • Both DWT and CWT effectively separated genuine arc signals from background noise.
  • Continuous Wavelet Transform (CWT) achieved superior localization accuracy with error margins from 0.0615 to 0.0713 m.
  • Discrete Wavelet Transform (DWT) showed localization errors between 0.0688 to 0.0789 m, while actual signal capture errors ranged from 0.0799 to 0.0844 m.

Conclusions:

  • The proposed integrated experimental and signal processing approach demonstrates high precision in arc fault detection and localization.
  • The study validates the effectiveness of wavelet transforms and ToA-based methods for enhancing power system safety.
  • Findings support the integration of experimental techniques for improved assessment of arcing conditions and addressing insulation system challenges.