Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/22679
Title: Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions
Authors: Mellit, Adel 
Kalogirou, Soteris A. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Deep learning;Fault detection and diagnosis;Internet of things;Machine learning;Photovoltaic systems;Remote sensing;Smart monitoring
Issue Date: Jun-2021
Source: Renewable and Sustainable Energy Reviews, 2021, vol. 143, articl. no. 110889
Volume: 143
Journal: Renewable and Sustainable Energy Reviews 
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.
URI: https://ktisis.cut.ac.cy/handle/10488/22679
ISSN: 1364-0321
DOI: 10.1016/j.rser.2021.110889
Rights: © Elsevier
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : University of Jijel 
AS-International Centre of Theoretical Physics 
Cyprus University of Technology 
Cyprus Academy of Science, Letters, and Arts 
Appears in Collections:Άρθρα/Articles

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