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Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection

Author

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  • Muhammad Hussain

    (Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

  • Hussain Al-Aqrabi

    (Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

  • Richard Hill

    (Centre for Industrial Analytics, Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK)

Abstract

This paper presents a framework for photovoltaic (PV) fault detection based on statistical, supervised, and unsupervised machine learning (ML) approaches. The research is motivated by a need to develop a cost-effective solution that detects the fault types within PV systems based on a real dataset with a minimum number of input features. We discover the appropriate conditions for method selection and establish how to minimize computational demand from different ML approaches. Subsequently, the PV dataset is labeled as a result of clustering and classification. The labelled dataset is then trained using various ML models before evaluating each based on accuracy, precision, and a confusion matrix. Notably, an accuracy ranging from 94% to 100% is achieved with datasets from two different PV systems. The model robustness is affirmed by performing the approach on an additional real-world dataset that exhibits noise and missing values.

Suggested Citation

  • Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection," Energies, MDPI, vol. 15(15), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5492-:d:875011
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    References listed on IDEAS

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    1. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    2. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.
    3. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
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    Cited by:

    1. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility," Energies, MDPI, vol. 15(22), pages 1-16, November.
    2. Wiktor Olchowik & Marcin Bednarek & Tadeusz Dąbrowski & Adam Rosiński, 2023. "Application of the Energy Efficiency Mathematical Model to Diagnose Photovoltaic Micro-Systems," Energies, MDPI, vol. 16(18), pages 1-24, September.
    3. Mohamed Trabelsi & Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Shady S. Refaat & Tingwen Huang & Fakhreddine S. Oueslati, 2022. "An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting," Energies, MDPI, vol. 15(23), pages 1-14, November.
    4. Tahir Hussain & Muhammad Hussain & Hussain Al-Aqrabi & Tariq Alsboui & Richard Hill, 2023. "A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision," Energies, MDPI, vol. 16(10), pages 1-19, May.

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