Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines

被引:74
|
作者
Abdelbaky, Mohamed Abdelkarim [1 ]
Liu, Xiangjie [1 ]
Jiang, Di [2 ]
机构
[1] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China
[2] Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Variable speed/pitch wind-turbine; Pitch control; Fuzzy model-predictive control; Linear matrix inequalities; Partial offline quasi-min-max; VARIABLE-SPEED; STABILITY;
D O I
10.1016/j.renene.2019.05.074
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the variable rotor speed and variable blade pitch wind-turbines system, the pitch controller is crucial in the high wind speed range to adjust the generator speed so as to produce the rated power. The main challenges in designing the pitch controller are the wind turbine's nonlinearities, constraints on pitch-angle, the variations in wind-speed, and the unstructured model dynamics. From this perspective, a new pitch controller is proposed by employing partial offline quasi-min-max fuzzy model-predictive control to investigate the variable-speed wind turbine performance. Based on the fuzzy modeling, the online optimization problem is simplified as a partial offline optimization problem (offline design and online synthesis). The key advantage of this controller is the guaranteed stability with actuator constraints using LMI constraints and less computational burden. Also, this controller is compared with standard gain scheduled-PI (proportional integral) controller, which has been utilized profusely in the wind-turbine industry. Furthermore, a typical 5 MW benchmark wind-turbine is employed to validate the results from the nonlinear mathematical model. Several case studies are made to prove the proposed controller effectiveness. The results show the superiority of the proposed controller over the gain scheduled-PI controller and two other advanced control techniques. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:981 / 996
页数:16
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