Abstract
This paper presents the design and simulation of an optimized fuzzy logic Maximum Power Point Tracking (MPPT) controller for grid-tied wind turbines, utilizing Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Four distinct methodologies are explored to determine the most suitable approach. Initially, the conventional perturb-and-observe technique is employed. Subsequently, the efficacy of fuzzy logic control without optimization is evaluated. Following this, FLC is enhanced by integrating particle swarm optimization and genetic algorithms. The novelty of employing fuzzy logic control optimized with PSO and GA lies in its ability to address inherent challenges in wind energy systems, such as varying wind speeds and grid voltage fluctuations. By combining the adaptability of fuzzy logic with the optimization systems of PSO and GA, our approach maximizes energy yield, ensures grid stability, and enhances overall system performance. This methodology represents a significant stride in renewable energy integration and grid management. To expedite the tuning process, the input and output membership function mappings are quickly adjusted to achieve the desired set point. Moreover, the mitigation of undesired occurrences such as distortion and abrupt wind speed variations is carefully reduced. A comparative analysis is provided between conventional P&O, standalone FLC, FLC-GA, and FLC-PSO to improve the wind energy conversion system. PSO and GA are capable of optimizing a fuzzy logic MPPT controller. PSO is often preferred for its quicker convergence, higher tracking accuracy, and lower computational complexity, resulting in more efficient and stable performance in wind energy conversion systems. PSO usually converges faster than GA, translating to a shorter transient time (0.05 s) when the system seeks the optimal power point in varying wind conditions. It results in a more responsive system, with faster adjustments to changes in wind speed.