MPPT Algorithms for photovoltaic systems under partial shading conditions

Abstract

This master's thesis delves into the comprehensive study of Photovoltaic (PV) systems, focusing on various aspects critical to their efficient operation and optimization. The work is divided into five chapters, each addressing a distinct facet of PV technology and control techniques. Chapter 1: provides a foundational understanding of PV systems, including an introduction to solar energy, photovoltaic cell technology, PV modeling, and the characteristics of solar cell I-V curves. In Chapter 2: the focus shifts to DC-DC converters, exploring the principles and operation of Boost and Buck converters, as well as the versatile Buck-Boost inverter converter. The advantages of Boost converters are also highlighted. Chapter 3: delves into the critical topic of Maximum Power Point Tracking (MPPT), elucidating the principles behind MPPT operation, typical MPPT-based PV system configurations, and the classification of MPPT algorithms, including indirect and direct methods. Chapter 4: introduces soft computing algorithms and novel techniques for MPPT, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Seagull Optimization Algorithm (SOA), and the innovative Guided Seagull Optimizer. These algorithms play a pivotal role in enhancing the performance of PV systems. Finally, in Chapter 5, the thesis presents simulation and experimental results to validate the effectiveness of the discussed algorithms under varying irradiance conditions, offering insights into their real-world applicability and performance.This research contributes to the growing body of knowledge surrounding PV systems, offering valuable insights into their operation, optimization, and control, with a particular emphasis on the application of soft computing techniques to maximize energy extraction.