Experimental Implementation of a Second-Order Adaptive Fuzzy Logic Controller for PMSG-Based Wind Energy Conversion Systems
Abstract
This study presents a second-order adaptive fuzzy logic controller (SO-AFLC) to enhance the dynamic performance of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs). The proposed controller simultaneously performs maximum power point tracking (MPPT), DC-link voltage regulation, and reactive power control and minimizes speed-tracking errors. Its performance is evaluated under step-changing wind conditions and measured wind speed data from Ras Gharib, Gulf of Suez, Egypt. A comprehensive comparison with conventional proportional-integral (PI) and adaptive fuzzy logic controller (AFLC) methods is conducted using MATLAB/Simulink. Compared with the AFLC and PI controllers, the proposed SO-AFLC achieves superior rotor speed tracking performance, with improvements of 36.98% and 53.26%, respectively. The proposed controller is further validated experimentally using a dSPACE DS1104 real-time platform. Additionally, an overall Integral Absolute Error (IAE)-based wind performance index is introduced to enable an objective comparison of the investigated controllers under identical operating conditions. SO-AFLC decreases the average of IAEs by 9.15% and 21.36% compared with the AFLC and PI controllers, respectively. Both simulation and experimental results demonstrate that the SO-AFLC provides faster transient response, higher tracking accuracy, and more reliable energy conversion, making it a promising solution for improving the grid integration of PMSG-based WECSs.