<![CDATA[The increasing demand for renewable energy in remote and off-grid areas has highlighted the importance of efficient wind energy utilization. However, wind energy systems are often challenged by unpredictable wind speeds, leading to suboptimal energy harvesting. This study proposes the design of a Firefly Algorithm (FA)-based Maximum Power Point Tracking (MPPT) system to enhance wind energy conversion efficiency. The objective is to develop an intelligent control strategy that optimizes the output power of a wind turbine under fluctuating wind conditions. The proposed system is implemented using a permanent magnet synchronous generator (PMSG) and a DC-DC boost converter. The Firefly Algorithm is employed to dynamically adjust the duty cycle of the converter, thereby maintaining operation at the maximum power point. Simulation results using MATLAB/Simulink demonstrate that the FA-based MPPT outperforms conventional Perturb and Observe (P&O) methods in terms of tracking speed, power stability, and efficiency. The proposed approach achieves faster convergence, reduced oscillations, and higher power output, making it highly suitable for deployment in remote areas with limited access to the power grid. These findings indicate that the integration of intelligent algorithms such as FA in MPPT systems can significantly enhance the performance of wind energy systems in challenging environments]]>
Copyrights © 2025
