Self-optimization of Wind Turbine Variable-pitch Control Parameters Based on Adaptive Genetic Algorithm

Abstract

For the low parameter tuning efficiency, accuracy and adaptability of the traditional PID parameter tuning method currently adopted in the wind turbine variable-pitch system, this paper presents a method for self-optimization of wind turbine variable-pitch control parameters based on adaptive genetic algorithm, including variable-gain variable-pitch PID parameter self-optimization and tower damping parameter self-optimization. The author first described the basic principle of variable-pitch control system; then based on the genetic algorithm, planned and designed constraint conditions for different wind turbine models, covering initial population selection, self-optimization algorithm constraints and adaptability function design; and finally compared and analyzed the response curve, simulation and field operation results of the traditional parameter tuning method and self-optimization parameter tuning method. The results show that, compared with the traditional parameter tuning method, the self-optimization parameter tuning method reduces the motor speed fluctuation by about 10%, and the fatigue My load of the tower bottom by about 2%. This shows the self-optimization parameter tuning method can improve the tuning efficiency, accuracy and adaptability.

Keywords

• wind turbine
• variable-gain variable-pitch pid control
• genetic algorithm
• self-optimization
• speed fluctuation
• fatigue my load of the tower bottom