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ML-Based Intermittent Fault Detection, Classification, and Branch Identification in a Distribution Network

Author

Listed:
  • Mojgan Hojabri

    (Competence Center of Digital Energy and Electric Power, Institute of Electrical Engineering, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

  • Severin Nowak

    (Competence Center of Digital Energy and Electric Power, Institute of Electrical Engineering, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

  • Antonios Papaemmanouil

    (Competence Center of Digital Energy and Electric Power, Institute of Electrical Engineering, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

Abstract

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6023-:d:1219111
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    References listed on IDEAS

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