Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

Tarek Berghout, Mohamed Benbouzid, Toufik Bentrcia, Xiandong Ma, Siniša Djurović and Leïla-Hayet Mouss
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Tarek Berghout: Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria
Mohamed Benbouzid: Institut de Recherche Dupuy de Lôme (UMR CNRS 6027), University of Brest, 29238 Brest, France
Toufik Bentrcia: Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria
Xiandong Ma: Engineering Department, Lancaster University, Lancaster LA1 4YW, UK
Siniša Djurović: Department of Electrical and Electronic Engineering, University of Manchester, Manchester M1 3BB, UK
Leïla-Hayet Mouss: Laboratory of Automation and Manufacturing Engineering, University of Batna 2, Batna 05000, Algeria

Energies, 2021, vol. 14, issue 19, 1-24

Abstract: To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

Keywords: photovoltaic systems; machine learning; deep learning; condition monitoring; faults diagnosis; fault detection; open source datasets (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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