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

Mojgan Hojabri, Samuel Kellerhals, Govinda Upadhyay and Benjamin Bowler
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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

Energies, 2022, vol. 15, issue 6, 1-18

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.

Keywords: photovoltaic system; PV faults; edge computing; machine learning; IOT; fault detection techniques; fault classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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