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A supervised ensemble learning method for fault diagnosis in photovoltaic strings

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  • Kapucu, Ceyhun
  • Cubukcu, Mete

Abstract

This study proposes a fault diagnosis method based on the use of a machine learning (ML) technique called ensemble learning (EL) for photovoltaic (PV) systems. EL methods aim to obtain better generalizability and prediction accuracy than a single ML algorithm by combining the predictions of multiple algorithms. In this context, first the most relevant features are selected by using grid-search with cross-validation. Then each learning algorithm and the EL model that will combine them have been improved in terms of parameter optimization.

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  • Kapucu, Ceyhun & Cubukcu, Mete, 2021. "A supervised ensemble learning method for fault diagnosis in photovoltaic strings," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s036054422100712x
    DOI: 10.1016/j.energy.2021.120463
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Adel Mellit & Chadia Zayane & Sahbi Boubaker & Souad Kamel, 2023. "A Sustainable Fault Diagnosis Approach for Photovoltaic Systems Based on Stacking-Based Ensemble Learning Methods," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
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    3. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
    4. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    5. Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
    6. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    7. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    8. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    9. Li, Jiangkuan & Lin, Meng & Wang, Bo & Tian, Ruifeng & Tan, Sichao & Li, Yankai & Chen, Junjie, 2024. "Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants," Energy, Elsevier, vol. 290(C).

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