Power enhancement using grey wolf optimizer algorithm for doubly fed induction generator based on fuzzy logic controller
1
Department of Electrical Engineering, Faculty of Technology/ LGE Research Laboratory, Mohamed Boudiaf University of M’sila (28000), Algeria
Submission date: 2025-05-29
Final revision date: 2025-11-22
Acceptance date: 2026-01-05
Online publication date: 2026-01-07
Publication date: 2026-01-07
KEYWORDS
TOPICS
ABSTRACT
The present research investigates innovative control technique for wind energy conversion systems using doubly fed induction generators, accentuating the limitations of traditional proportional-integral controllers under variable wind conditions. It examines the integration of nonlinear fuzzy logic controllers to improve robustness, stability, and power quality. Furthermore, a fuzzy grey wolf optimizer algorithm is employed to optimally tune controller gains, optimizing both active and reactive power regulation. The research models the wind turbine, generator, machine and grid-side converters, with MATLAB/Simulink program validating the efficiency of the proposed technique. Results demonstrate that the integrated fuzzy-GWO controller significantly outperforms conventional methods. Specifically, for active power control, it reduces the Integral of Time-weighted Absolute Error (ITAE) by 98.9% compared to the PI controller and by 95.8% compared to the Fuzzy PD controller. This translates to a faster response with negligible overshoot and superior tracking accuracy under both steady-state and variable wind conditions, thereby improving the efficiency and reliability of wind energy systems.
FUNDING
This research did not receive any outside financial support.
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| eISSN: | 2449-5220 |
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