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

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.