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

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 m...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.45583-45593
Main Authors: Xiaoqing, Li, Haiying, Dong, Hongwei, Li, Mingxue, Li, Zhiqiang, Sun
Format: Article
Language:English
Subjects:
Algorithms
Control algorithms
Control theory
Converters
Crossovers
Front-end speed regulation (FESR) wind turbine
Gears
hybrid automata
Hydraulic systems
improved NSGA-II algorithm
Machine learning
Mutation
Optimal control
Optimization
optimization control
Pitch (inclination)
power control
Probability distribution functions
Smoothness
Torque
Wheels
Wind speed
Wind turbines
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2908995