A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System. (28th September 2022)

• Record Type: Journal Article
• Authors: Prasad, Kalapala; Samson Isaac, J.; Ponsudha, P.; Nithya, N.; Shinde, Santaji Krishna; Gopal, S. Raja; Sarojwal, Atul; Karthikumar, K.; Hadish, Kibrom Menasbo
• Other Names: Ramesh Bapu BR Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2022(2022)
• Publication Date: 2022-09-28
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2022/2845755
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : 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.