Advanced Control Of a Renewable Energy System

Abstract

This thesis investigates advanced control strategies for photovoltaic (PV) systems to address challenges arising from climate variations and operational conditions in both stand-alone and grid-connected configurations. The research is divided into four main chapters: an overview of PV generators, power electronics design, intelligent control techniques, and metaheuristic algorithms for maximum power point tracking (MPPT) under partial shading conditions (PSC). The study begins by analyzing the behavior of PV generators and their integration with DC/DC converters. Intelligent control methods, including Perturb and Observe (P&O) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are employed to enhance MPPT efficiency under varying irradiance levels, with ANFIS demonstrating superior performance. Hybrid approaches combining Fuzzy Logic Control (FLC) and Artificial Neural Networks (ANN) are compared against Incremental Conductance (IC) methods for grid-connected systems, highlighting the hybrid controller’s ability to track maximum power dynamically without prior PV system information. For fault diagnosis, a neural network (NN) model achieves high accuracy in detecting PV faults. Lastly, metaheuristic MPPT controllers—Grey Wolf Optimization (GWO), PSO, and Adaptive PSO (APSO)—are evaluated under PSC. The APSO algorithm excels in achieving fast and efficient MPPT. Simulations conducted using MATLAB/Simulink validate the effectiveness of the proposed methods, showcasing their potential for real-world applications in renewable energy systems.