Optimization of Torque Control Parameters for Wind Turbine Based on Drive Chain Active Damping Control

• Published in: IEEE access Vol. 10; pp. 106482 - 106494
• Main Authors: Liu, Yingming, Zhang, Shuyuan, Wang, Xiaodong, Gao, Xing, Cao, Tian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Active control; Active damping; Adaptive control; Bandpass filters; Chain drives; Chains; Control methods; control parameters optimization; Damping; drive chain active damping control; Dynamic models; Generators; Genetic algorithms; hierarchic genetic algorithm; Inertia; Mutual coupling; Optimization; Parameter estimation; Parameter identification; Proportional integral; Resonant frequency; system identification; Torque; Torque control; Torque measurement; Vibration; Wind effects; Wind speed; Wind turbines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3212155

Considering the mutual coupling effect of wind turbine control parameters, an optimization method of the control parameters for torque system based on drive chain active damping control is proposed, which improves the control accuracy and solves the problem that the control objectives are difficult to coordinate. Firstly, the mathematical model of the torque-speed system and the dynamical model of the drive chain active damping control are established. At the same time, the initial value of active damping gain is calculated according to the designed band-pass filter measurement results. Secondly, to facilitate the setting of proportional-integral (PI) control parameters, the Routh method is adopted to identify the torque-speed system with large inertia characteristic as a low-order inertia system. Subsequently, the Integrated Time and Absolute Error (ITAE) criterion is used to set the initial value of the PI control parameters for the torque system. Furthermore, the PI control parameters and active damping gains at each equilibrium point are optimized based on the Hierarchic Genetic Algorithm, which improves the accuracy of control parameters. At the same time, the control objectives are coordinated by optimizing weight allocation based on the Pareto. Then the adaptive control method is constructed by fitting them with the wind speed to improve the control accuracy far from the equilibrium points. Finally, the effectiveness of the proposed method is verified by comparing the frequency-domain characteristic, control accuracy, twist vibration of the drive chain, tower vibration and load.