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Cited by 280Electrical Fault Detection and ProtectionPhotovoltaic System Optimization TechniquesFault Detection and Control Systems

The DOI matches its Crossref record; 115 of the 117 references it cites were found in a registry or an index.

Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions

Open access
Mellit, Adel, Kalogirou, Soteris·2021·Renewable and Sustainable Energy Reviews
DOI 10.1016/j.rser.2021.110889
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Abstract

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.

Overview

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Crossref · 10.1016/j.rser.2021.110889

Cites

115 of 115 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 117 references

Research works printed but not linked: 2

  1. Hamied, 2019, IoT-based smart photovoltaic arrays for remote sensing and fault identification. International conference in artificial intelligence in renewable energetic systems, 478
  2. Dupont, 2019, 200, 112078

Cited by

Items 201–269 of 269

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Metadata

Authors
Mellit, A., & Kalogirou, S.
Affiliations
  • University of Jijel (DZ)
  • Cyprus University of Technology (CY)
Corresponding author
Mellit, Adel
ORCID
Venue
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Published
2021-06
Volume
143
Pages
110889
ISSN
1364-0321 (ISSN-L)
DOI
10.1016/j.rser.2021.110889
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
Licence
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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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.