Contribution to the MPPT Algorithms by Integrating Bio-Inspired Optimization in Photovoltaic Systems

Abstract

This thesis investigates advanced modeling and control strategies to enhance the performance of photovoltaic systems under variable environmental conditions. An adaptive photovoltaic model based on fuzzy logic with genetically optimized rules is proposed to accurately represent the nonlinear behavior of PV generators, achieving improved prediction accuracy and robustness under dynamic irradiance and temperature variations. In parallel, an enhanced indirect MPPT approach combining a (P&O) algorithm with a Grasshopper Optimization Algorithm–tuned Tilted IntegralDerivative controller is developed using an experimentally identified small-signal model. The proposed control strategy ensures improved voltage regulation and reduced oscillations around the maximum power point. Simulation and experimental results confirm the effectiveness and robustness of the proposed approaches compared to conventional techniques.