Optimization of solar energy using artificial neural network vs recurrent neural network controller with positive output super lift Luo converter

Kasim Ali Mohammad * and Sarhan M. Musa

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), 133–154.
Article DOI: 10.30574/wjaets.2024.12.2.0289
Publication history: 
Received on 31 May 2024; revised on 08 July 2024; accepted on 11 July 2024
 
Abstract: 
In today’s world, the need for clean energy is crucial. Historically, Renewable energy sources like hydropower, wind, and solar offer sustainable solutions. Photovoltaic (PV) systems convert sunlight into electricity using semiconductor PV cells, which have been efficient for over 30 years. PV cell efficiency depends on irradiance (solar photon intensity) and temperature. Higher irradiance increases efficiency, while higher temperatures decrease it. PV systems, despite low voltage outputs, can be optimized using DC-DC Positive Output Super Lift Luo converters to match load requirements, enhancing system efficiency. Solar irradiance varies throughout the day, affecting PV cell output. Maximum Power Point Trackers (MPPTs) adjust the system's operating point to maintain peak efficiency. This study focuses on designing AI controllers to manage MPPT. We compare the performance of Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) using three datasets. The goal is to identify the most efficient AI controller for optimizing solar energy systems.
 
Keywords: 
Artificial neural network; DC-DC positive output super lift luo converter; Maximum power point tracking; Photovoltaic system; Recurrent neural network
 
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