Maximum Power Point Tracking for Photovoltaic Panels Based on Fuzzy Logic and Artificial Neural Network Algorithms

The abstract summarizes a research study focused on improving the efficiency of Photovoltaic (PV) systems through an innovative Maximum Power Point Tracking (MPPT) technique, particularly designed for Partial Shading Conditions (PSC). Here's a breakdown of the key points from the abstract: Importance of MPPT: The abstract emphasizes the significance of MPPT controllers in enhancing the efficiency of PV systems. Challenges under Partial Shading Conditions (PSC): Conventional MPPT techniques are acknowledged to face challenges when dealing with partial shading conditions, where the irradiance on the PV panels is not uniform. Proposed MPPT technique: The study introduces a novel MPPT technique designed specifically for PSC. This technique combines two elements: A) Artificial Neural Networks (ANNs) can be used for a wide range of prediction the area where the Global Maximum Power Point (GMPP) is likely to be located. B) Perturb and Observe (P and O) algorithm: Employed to precisely determine the exact position of the GMPP within the predicted area validation through computer simulations: To assess the effectiveness of this new technique, computer simulations were conducted using the MATLAB/Simulink program. The results of these simulations confirmed that the proposed MPPT method can track the GMPP faster compared to other existing techniques. In summary, this research aims to address the limitations of conventional MPPT techniques in partial shading conditions by introducing an approach that combines ANN prediction with the classic P and O algorithm. The computer simulations validate the effectiveness of this method in improving the efficiency of PV systems under challenging shading conditions.