BAT-Optimized PID and ANFIS Torque-Based MPPT for a 0.5 MW PMSG-Based Wind Energy Conversion System Under Dynamic Wind Conditions - ODEYEMI, Emmanuel T.; HARUNA, Ya’u S.; BAKARE, Ganiyu A.; MAMMAN, Hassan B.; HASSAN, Sabo M. (FUDMA Journal of Engineering and Technology, 2026)
Efficient maximum power point tracking (MPPT) in variable-speed wind energy conversion systems (WECS) is challenged by nonlinear turbine–generator dynamics and rapidly varying wind conditions, which limit the effectiveness of conventional controllers. This paper proposes an intelligent torque-based MPPT framework for a 0.5 MW grid-connected permanent magnet synchronous generator (PMSG) WECS, combining metaheuristic optimization with adaptive neuro-fuzzy control. A high-fidelity MATLAB/Simulink model integrating turbine aerodynamics, generator dynamics, and power electronic conversion is developed for systematic evaluation. Three controllers; conventional PID, BAT-optimized PID (BAT-PID), and BAT-optimized adaptive neuro-fuzzy inference system (BAT-ANFIS), are comparatively assessed under step, ramp, and turbulent wind profiles. Results show that BAT optimization improves PID transient performance by 25–30% and reduces steady-state error to 5.2%. The proposed BAT-ANFIS further achieves a 40–45% reduction in settling time relative to conventional PID and a 15–20% improvement over BAT-PID, while maintaining DC-link voltage within ±1% of nominal (950–970 V). Torque ripple is reduced by 30%, and MPPT efficiency reaches 100% under ramp conditions and ≈98% under turbulent profiles, outperforming BAT-PID (≈86–96%) and conventional PID (≈78–88%). These results demonstrate that integrating BAT-based optimization with neuro-fuzzy adaptation significantly enhances dynamic response, robustness, and real-time power extraction, establishing BAT-ANFIS as a high-performance MPPT solution for large-scale wind energy systems.