Published Paper Details:

In the context of renewable energy generation, efficient extraction of power from wind resources is of paramount importance. Maximum Power Point Tracking (MPPT) techniques play a crucial role in optimising the output of Wind Energy Conversion Systems (WECS), especially under fluctuating wind conditions. This study presents a comparative evaluation of two MPPT strategies--Perturb and Observe (P&O) and the Genetic Algorithm (GA)--implemented in a wind energy system equipped with a Permanent Magnet Synchronous Generator (PMSG). The PMSG has been chosen due to its high efficiency, robustness, and suitability for direct-drive configurations, eliminating the need for gearboxes. The complete system is modelled and simulated using MATLAB/Simulink, considering a range of dynamic wind speed conditions. The performance of both MPPT techniques is analysed based on their ability to track the maximum power point efficiently. The simulation results indicate that while the conventional P&O method achieves a maximum output of 1049 kW and performs well under steady conditions, it exhibits limitations in terms of response time and output oscillations during wind fluctuations. In contrast, the GA-based MPPT algorithm showcases significantly improved performance, achieving a higher power output of 2785 kW, along with faster convergence and smoother power tracking. The study highlights the potential of advanced, intelligent optimisation methods like Genetic Algorithms in enhancing the reliability and effectiveness of modern wind energy systems, particularly in real-world operating scenarios marked by dynamic environmental changes.