Optimization Control of Front-End Speed Regulation (FESR) Wind Turbine Based on Improved NSGA-II

Abstract

Aiming at the power optimization control problem of front-end speed regulation (FESR) wind turbine, when the wind speed above rated wind speed, an optimal control strategy of variable pitch and variable torque based on improved NSGA-II algorithm is proposed. For this strategy, the reverse learning mechanism was used to initialize and evolve, and the crossover probability and mutation probability were adjusted dynamically based on the exponential distribution. A fuzzy comprehensive evaluation was used to get the best solution from the non-inferior solutions. The objective function was established upon the goals of the constant output speed for hydraulic speed converter and the smoothness of FESR's output power. And pitch angle and vane opening angle were optimized by the improved NSGA-II algorithm. Afterward, the optimization control algorithm has been validated through simulation experiment to a 2 MW FESR. The simulation results show that the output power of FESR was smoothed, and the speed fluctuation and power fluctuation of FESR were also reduced, better control performance is thus achieved. Therefore, it is feasible and effective to optimize the control of FESR by the improved NSGA-II algorithm proposed in this paper.