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Research Article

Convolutional Neural Network based Automatic Detection of Visible Faults in a Photovoltaic Module

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Received 05 Oct 2020, Accepted 10 Mar 2021, Published online: 29 Mar 2021

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Huan Fu & Guoqing Cheng. (2023) Convolutional Neural Network based Efficient Detector for Multicrystalline Photovoltaic Cells Defect Detection. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 45:3, pages 8686-8702.
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Pulavarthi Satya Venkata Kishore, Jami Rajesh, Nakka Jayaram & Sukanta Halder. (2022) A Survey of Machine Learning Applications in Renewable Energy Sources. IETE Journal of Research 0:0, pages 1-18.
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Dhritiman Adhya, Soumesh Chatterjee & Ajoy Kumar Chakraborty. (2022) Stacking ensemble based fault diagnosis approach for improved operation of photovoltaic arrays. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44:2, pages 5421-5439.
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Naveen Venkatesh Sridharan & Vaithiyanathan Sugumaran. (2022) Deep learning-based ensemble model for classification of photovoltaic module visual faults. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 44:2, pages 5287-5302.
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Naveen Venkatesh Sridharan & Vaithiyanathan Sugumaran. (2021) Visual fault detection in photovoltaic modules using decision tree algorithms with deep learning features. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 0:0, pages 1-17.
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