A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System

Autor: Kalapala Prasad, J. Samson Isaac, P. Ponsudha, N. Nithya, Santaji Krishna Shinde, S. Raja Gopal, Atul Sarojwal, K. Karthikumar, Kibrom Menasbo Hadish
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Photoenergy, Vol 2022 (2022)
Druh dokumentu: article
ISSN: 1687-529X
DOI: 10.1155/2022/2845755
Popis: Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results.
Databáze: Directory of Open Access Journals
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