Optimized non-linear–PI controller design for photovoltaic (PV) based distributed generation (DG) integration and islanding detection against microgrid application

This thesis presents a comprehensive study on the integration and control of a 400-kW photovoltaic (PV) farm connected to a 25 kV main grid, focusing on developing advanced control strategies to enhance stability, efficiency, and reliability. Distributed Generation (DG), particularly through solar energy, has become increasingly vital for its potential to improve energy security, reduce environmental impact, and support decentralized power generation. The research begins with an extensive review of existing literature on PV systems, control strategies, Maximum Power Point Tracking (MPPT) techniques, islanding detection methods, and stability analysis, identifying gaps in current approaches that underscore the need for innovative solutions to improve the performance and reliability of PV systems. To address these challenges, a novel adaptive fuzzy Proportional-Integral (PI) control strategy is proposed. This controller integrates fuzzy logic with traditional PI control to dynamically adjust control parameters, thereby enhancing stability and fault tolerance. The methodology involves dynamic modeling of MPPT and Voltage Source Converter (VSC) systems, the development of a nonlinear control strategy, and thorough simulation-based validation using MATLAB/SIMULINK. The results demonstrate that the proposed adaptive fuzzy PI control significantly outperforms conventional PI and fuzzy PI controls in terms of stability, efficiency, and fault tolerance. Various case studies, including different solar irradiance levels, three-phase ground faults, and islanding scenarios, validate the robustness of the control strategy. Furthermore, this research delves into the integration of PV arrays with VSC devices for Distributed Generation in Microgrid applications, highlighting the stability concerns associated with conventional VSC dynamics and the necessity for a more sophisticated control approach. The developed adaptive-based Optimized Fuzzy Logic - Proportional-Integral (FL-PI) controller addresses these issues, excelling in small-signal stability and dynamic oscillations. The study employs comprehensive transient and steady-state simulations to evaluate system performance under various fault conditions and PV irradiance fluctuations, with the "400-kW Grid-Connected PV Farm" model serving as a significant platform for detailed research and performance evaluation, providing critical insights into renewable energy integration. In conclusion, this research makes significant contributions to the field of PV system control methodologies by offering a robust solution for enhancing the stability and performance of grid-connected PV farms. The findings support the integration of renewable energy sources into the grid, promoting sustainable and reliable energy systems. This new proposed innovative controller validated through extensive data analysis and modeling, demonstrates its effectiveness in addressing stability issues and optimizing performance under dynamic conditions, paving the way for future advancements in control methods and renewable energy integration in Microgrid environments.