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IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods

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)

  • Samuel Kellerhals

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

  • Govinda Upadhyay

    (SmartHelio Sarl, 1012 Lausanne, Switzerland)

  • Benjamin Bowler

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

Abstract

Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.

Suggested Citation

  • Mojgan Hojabri & Samuel Kellerhals & Govinda Upadhyay & Benjamin Bowler, 2022. "IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods," Energies, MDPI, vol. 15(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2097-:d:770227
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    References listed on IDEAS

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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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