Management of power in single rotor wind turbine systems using fuzzy controller based on fractional order error approaches

Fuzzy Logic (FL) is a well-established artificial intelligence technique, particularly valuable in control applications where system modeling is either highly complex or impractical. However, its dependence on heuristic knowledge and rule-based decision-making can limit its precision and adaptability in dynamic environments. To address these challenges, this study introduces an enhanced FL-based control strategy that incorporates fractional-order error (FOE) to improve control performance in a single-rotor wind turbine system. The proposed FOE-based FL approach is applied to overcome the problems of the direct power control (DPC) of doubly-fed induction generators (DFIGs), leveraging fractional calculus to enhance system response, robustness, and efficiency. Extensive MATLAB-based simulations validate the effectiveness of the DPC-FOE-FL method with pulse width modulation compared to conventional DPC-FL control. The comparative analysis reveals that the proposed method significantly reduces energy fluctuations and harmonic distortion in the stator current, achieving a 40.85% reduction in the first test and a 34.21% reduction in the second test relative to traditional DPC-FL control. Additionally, the DPC-FOE-FL approach effectively suppresses reactive power overshoot, demonstrating reductions of 96.28%, 96.27%, and 54.56% across multiple test scenarios. Active power ripples are also minimized by 64.37%, 62.59%, and 62.96%, highlighting the method's superior dynamic performance. These findings confirm that integrating FOE into FL-based controllers significantly enhances power control stability and efficiency in wind energy systems. The DPC-FOE-FL technique offers a promising solution for optimizing renewable energy applications, particularly in fluctuating wind conditions, by ensuring greater precision, adaptability, and robustness in power regulation.