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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with

Xin Ning1,2, Tianli Ding3, Hongwei Zhu3

  • 1State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
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This summary is machine-generated.

This study introduces an advanced method for detecting hazardous arc faults in power systems by integrating multiple data features. The new approach enhances safety and system stability with improved accuracy and lower computational costs.

Area of Science:

  • Electrical Engineering
  • Power Systems Safety
  • Artificial Intelligence in Engineering

Background:

  • Arc faults pose significant fire and safety risks in power systems.
  • Existing arc fault detection methods often struggle with cost-effective feature selection.
  • Accurate and timely detection is critical for preventing accidents and ensuring grid stability.

Purpose of the Study:

  • To develop a superior arc fault detection method addressing limitations in feature selection.
  • To improve the accuracy and reduce the misjudgment rate of arc fault detection.
  • To offer a computationally efficient solution for arc fault detection.

Main Methods:

  • A multi-feature approach combining time-domain, frequency-domain, energy, and spatial features.
Keywords:
CNNarc faultconditional batch normalizationcost-sensitive optimization

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  • Integration of these features into a conditional batch normalization (CBN) convolutional neural network.
  • Comparative analysis against traditional arc fault detection models.

    • Main Results:

    • The proposed multi-feature CBN method demonstrated superior detection performance.
    • Achieved higher accuracy and a lower misjudgment rate compared to traditional models.
    • Maintained a lower computational cost, indicating efficiency.

    Conclusions:

    • The developed method provides an effective improvement for arc fault detection.
    • The multi-feature integration and CBN network offer enhanced safety and operational stability.
    • This approach presents a viable, cost-effective solution for critical power system safety.