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A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System
oleh: Kalapala Prasad, J. Samson Isaac, P. Ponsudha, N. Nithya, Santaji Krishna Shinde, S. Raja Gopal, Atul Sarojwal, K. Karthikumar, Kibrom Menasbo Hadish
Format: | Article |
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Diterbitkan: | Wiley 2022-01-01 |
Deskripsi
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.