Optimization of Solar Energy using Artificial Neural Network VS Recurrent Neural Network Controller with Ultra Lift Luo Converter
Keywords:
waveguide, refraction index, triangular element, eigenmode, eigenmatrix equation, edge, node, electric equipotential, focus., online hospital services; technology acceptance model; perceived usefulness; perceived ease of use; trust, Artificial Neural Network., dc-dc ultra lift luo converter, maximum power point tracking, photovoltaic system, recurrent neural network.Abstract
In today’s society, the demand for clean energy is essential. Traditionally, renewable sources such as hydropower, wind, and solar have provided sustainable solutions. Photovoltaic (PV) systems generate electricity from sunlight using semiconductor PV cells, which have been effective for over 30 years. The efficiency of PV cells depends on irradiance (solar photon intensity) and temperature. Higher irradiance boosts efficiency, while higher temperatures reduce it. Despite their low voltage outputs, PV systems can be optimized with DC-DC Ultra Lift Luo converters to meet load requirements, improving system efficiency. The Ultra Lift Luo converter, a type of DC-DC converter, offers a higher voltage conversion gain than conventional boost converters. This converter belongs to the Luo converter family, which uses advanced techniques to achieve high voltage gain and efficiency. Solar irradiance fluctuates throughout the day, impacting PV cell output. Maximum Power Point Trackers (MPPTs) adjust the system’s operating point to sustain peak efficiency. This study aims to design AI controllers for MPPT management. In addition, we evaluate the performance of Artificial Neural Networks (ANN) and Recurrent Neural Networks (RNN) with three datasets to determine the most efficient AI controller for optimizing solar energy systems.
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