Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator

The main challenge in improving energy harvest faced by modern large-scale wind turbines (WTs) comes from the fact that the blade rotor with the large inertia having a slow response impedes the optimal tip speed ratio tracking. On the premise that the future wind speed can be accurately predicted, nonlinear model predictive control (NMPC) can effectively improve the energy harvest efficiency of large WTs, but it is difficult to maximize the energy capture and minimize the torque fluctuation of the generator simultaneously. In this study, a fuzzy regulator is designed to update the weight coefficient of the cost function, and an improved multi-objective marine predator algorithm (IMMPA) is proposed to optimize the fuzzy regulator, so it is realized the coordinated optimization on energy capture and generator torque fluctuation. Specifically, based on the analysis of the performance of NMPC with fixed weight coefficient under different wind conditions, a basic fuzzy regulator is designed to adjust the weight coefficient according to the characteristics of wind conditions, and four groups of candidate inputs are defined. In order to fully explore the potential of the fuzzy regulator, IMMPA is used to optimize the membership function of the fuzzy regulator. Finally, the variable weight coefficient is used as the input of NMPC to update the objective function in real time. The simulation results show that compared with the one with the fixed weight coefficient, the NMPC controller using the variable weight improves the energy capture by 1.77% while reducing the generator torque fluctuation by 0.126%. Taking the concerned 1.5 MW WT as an example, the variable-weight NMPC could boost its annual energy production by 35842.5 kWh in comparison with the fixed-weight counterpart, showing its promising role in reducing the production cost for the WTs.