Application of AI-Based Algorithms for Industrial Photovoltaic Module Parameter Extraction

• Published in: SN computer science Vol. 4; no. 5; p. 525
• Main Authors: Kumar, Vandrasi Raja, Bali, Sravana Kumar, Devarapalli, Ramesh
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2023; Springer Nature B.V
• Subjects: Accessibility; Algorithms; Circuit design; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Diodes; Enabling Innovative Computational Intelligence Technologies for IOT; Equivalent circuits; Females; Heuristic; Information Systems and Communication Service; Literature reviews; Optimization algorithms; Original Research; Parameter estimation; Parameters; Pattern Recognition and Graphics; Photovoltaic cells; Power efficiency; Renewable energy sources; Software Engineering/Programming and Operating Systems; Solar cells; Solar energy; Vision; AI; Parameter; Meta-heuristic; PV module; Single diode
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-023-02008-4

Solar energy is the best choice in non-renewable energy sources for generating electricity since it is a widely accessible and sustainable source. Solar energy is now among the useful substitute energy sources that readily exist on the energy market because of recent advances in photovoltaic (PV) expertise. The enhancement of power efficiency of PV systems is a significant priority of the research community and industry to make solar energy more accessible and cost-effective. A solar cell's circuit model is non-linear and transcendental with some unknown parameters. The electrical equivalent circuit of industrial solar photovoltaic modules has been designed using the experimental results from the datasets. This paper compares novel AI-based algorithms for industrial photovoltaic module parameter extraction and presents detailed analysis, including state-of-the-art approaches.