New Design of Robust Model Predictive Control for Wind Power System Using Frequency Domain Bands and Dandelion-Optimizer

Wind power systems often face challenges due to uncertainties in load demands and system parameters, which can affect their stability and performance. This article addresses these challenges by introducing a design of robust model predictive control (RMPC) tailored to address system uncertainties effectively. To manage uncertainty, this article formulates new frequency domain bands derived from the Hermite–Biehler theorem, ensuring the stability of wind power system amidst varying load demands and system parameters. Furthermore, the tuning of RMPC parameters, such as prediction horizon, control horizon, sample rate, and weighting factors, are optimized using an innovative dandelion-optimizer (DO) algorithm, which incorporates frequency domain bands as inequality constraints during parameter adjustment. The efficacy of the proposed RMPC design is validated using fuzzy logic (FL) and adaptive network-based fuzzy Inference system (ANFIS), demonstrating its superiority over traditional methods. Comparative assessments with other optimization algorithms from the literature highlight the effectiveness of the DO algorithm. In addition, the integral absolute error (IAE) index for the proposed RMPC is 0.0388. This value is significantly lower than the IAE values of 0.0507 for ANFIS and 0.4555 for FL control methods. This reduction in IAE demonstrates the enhanced performance and accuracy of the proposed approach when compared to traditional control strategies. Comprehensive testing under various load demand and system parameters variations substantiates the method's robustness and superior damping performance better than other existing methods.