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Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions
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Abstract Currently, a huge number of photovoltaic plants have been installed worldwide and these plants should be carefully protected and supervised continually in order to be safe and reliable during their working lifetime. Photovoltaic plants are subject to different types of faults and failures, while available fault detection equipment are mainly used to protect and isolate the photovoltaic plants from some faults (such as arc fault, line-to-line, line-to-ground and ground faults). Although a good number of international standards (IEC, NEC, and UL) exists, undetectable faults continue to create serious problems in photovoltaic plants. Thus, designing smart equipment, including artificial intelligence and internet of things for remote sensing and fault detection and diagnosis of photovoltaic plants, will considerably solve the shortcomings of existing methods and commercialized equipment. This paper presents an overview of artificial intelligence and internet of things applications in photovoltaic plants. This research presents also the most advanced algorithms such as machine and deep learning, in terms of cost implementation, complexity, accuracy, software suitability, and feasibility of real-time applications. The embedding of artificial intelligence and internet of things techniques for fault detection and diagnosis into simple hardware, such as low-cost chips, may be economical and technically feasible for photovoltaic plants located in remote areas, with costly and challenging accessibility for maintenance. Challenging issues, recommendations, and trends of these techniques will also be presented in this paper.
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Cited by
Failures of Photovoltaic modules and their Detection: A Review
Akram, M. W., Li, G., Jin, Y., & Chen, X. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118822 · Cited by 212
Photovoltaic system fault detection techniques: a review
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Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
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Overview of Demand-Response Services: A Review
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Metadata
- Authors
- Mellit, A., & Kalogirou, S.
- Affiliations
- University of Jijel (DZ)
- Cyprus University of Technology (CY)
- Corresponding author
- Mellit, Adel
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 143
- Pages
- 110889
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3134391797
- Cited by
- 280
- References
- 117
- Citations by year
- 2026: 45 · 2025: 69 · 2024: 61 · 2023: 49 · 2022: 38 · 2021: 17
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- green
- ktisis.cut.ac.cy
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- photovoltaic systems, fault detection and diagnosis, Internet of Things, artificial intelligence, remote sensing, machine learning, deep learning, low-cost hardware, remote areas
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
