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High Impedance Fault Detection Protection Scheme for Power Distribution Systems

Author

Listed:
  • Katleho Moloi

    (Department of Electrical Power Engineering, Durban University of Technology, Durban 4000, South Africa)

  • Innocent Davidson

    (Department of Electrical Power Engineering, Durban University of Technology, Durban 4000, South Africa)

Abstract

Protection schemes are used in safe-guarding and ensuring the reliability of an electrical power network. Developing an effective protection scheme for high impedance fault (HIF) detection remains a challenge in research for protection engineers. The development of an HIF detection scheme has been a subject of interest for many decades and several methods have been proposed to find an optimal solution. The conventional current-based methods have technical limitations to effectively detect and minimize the impact of HIF. This paper presents a protection scheme based on signal processing and machine learning techniques to detect HIF. The scheme employs the discrete wavelet transform (DWT) for signal decomposition and feature extraction and uses the support vector machine (SVM) classifier to effectively detect the HIF. In addition, the decision tree (DT) classifier is implemented to validate the proposed scheme. A practical experiment was conducted to verify the efficiency of the method. The classification results obtained from the scheme indicated an accuracy level of 97.6% and 87% for the simulation and experimental setups. Furthermore, we tested the neural network (NN) and decision tree (DT) classifiers to further validate the proposed method.

Suggested Citation

  • Katleho Moloi & Innocent Davidson, 2022. "High Impedance Fault Detection Protection Scheme for Power Distribution Systems," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4298-:d:974863
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    References listed on IDEAS

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    1. Khushwant Rai & Farnam Hojatpanah & Firouz Badrkhani Ajaei & Katarina Grolinger, 2021. "Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders," Energies, MDPI, vol. 14(12), pages 1-25, June.
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    Cited by:

    1. Mojgan Hojabri & Severin Nowak & Antonios Papaemmanouil, 2023. "ML-Based Intermittent Fault Detection, Classification, and Branch Identification in a Distribution Network," Energies, MDPI, vol. 16(16), pages 1-15, August.

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