Artificial Neural Network-Based Energy Management System for Optimized Solar Photovoltaic Performance

The purpose of this research is to offer an improved maximum power point tracking (MPPT) method that makes use of artificial neural networks (ANN) in order to maximise the effectiveness of photovoltaic (PV) systems.  Through the process of estimating and adjusting the duty cycle of the DC-DC converter in order to track the maximum power point, the suggested ANN controller is able to regulate itself in response to climatic circumstances (irradiance and temperature).  For the purpose of training the Maximum Power Point Tracking (MPPT) algorithm, measurements from a perturb and observe (P&O) algorithm are recorded under a range of different climatic circumstances.  The usefulness of the suggested technique is demonstrated by simulation and experimental findings, which show that the new method is more efficient, has fewer oscillations, and has less overshoot than traditional P&O MPPT methods.  The performance of the proposed method has been confirmed by experimental verification using the DC-DC Converter and the supporting platform under steady-state conditions.