Optimization of solar energy using recurrent neural network controller with dc-dc boost, Cuk, and single-ended primary inductor converter (SEPIC) Converters
Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, TX, USA.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(02), 257-269.
Article DOI: 10.30574/wjaets.2024.12.2.0313
Publication history:
Received on 07 June 2024; revised on 15 July 2024; accepted on 18 July 2024
Abstract:
The pressing issue of the greenhouse effect demands strategies to reduce carbon dioxide (CO2) emissions, a detrimental gas with widespread adverse effects. The sun, as the ultimate renewable energy source, generates energy without CO2 emissions. Harnessing solar power necessitates a photovoltaic (PV) system equipped with a Maximum Power Point Tracker (MPPT) to optimize energy output. The MPPT adapts to changing environmental conditions and communicates through a Pulse Width Modulator (PWM) to an Insulated Gate Bipolar Transistor (IGBT), which alters its duty cycle to align system resistance with the load. Traditional Perturbation and Observation (P&O) algorithms struggled with environmental variations, but advanced AI-based Recurrent Neural Network (RNN) controllers enhance efficiency. This research compares RNN controllers using three data sets of 104, 201, and 1001 entries with three DC-DC converters: Boost, Cuk, and Single-Ended Primary Inductor Converter (SEPIC).
Keywords:
Dc-DC boost converter; DC-DC cuk converter; DC-DC single-ended primary inductor converter; Maximum power point tracking; Photovoltaic system; Recurrent neural network
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0