Kalman filter-based wind speed estimation for wind turbine control

被引:1
作者
Dongran Song
Jian Yang
Mi Dong
Young Hoon Joo
机构
[1] Central South University,School of Information Science and Engineering
[2] Kunsan National University,Department of Control and Robotics Engineering
来源
International Journal of Control, Automation and Systems | 2017年 / 15卷
关键词
Extended Kalman filter; Kalman filter; wind speed estimation; wind turbine;
D O I
暂无
中图分类号
学科分类号
摘要
To improve power production and reduce loads on turbine components, exact wind speed information is required in modern wind turbine controllers. However, the wind speed measured on the nacelle is imprecise because of its drawbacks of single point measurement and non-immunity to disturbances. To solve this problem, the EWS (Effective Wind Speed) estimator has been proposed as an alternative. According to the literatures, there are two kinds of EWS estimator, that is, the KF-based estimator and the EKF-based one. Where, the former is applied to estimate the aerodynamic torque, then the EWS is numerically calculated; and the latter directly estimate the EWS. Since the estimate EWS significantly affect the controller’s effectiveness, their performance needs to be clarified. To fully investigate the two estimators, there is a need to evaluate their performance on an even platform. In this paper, we present comparative studies on these two methods. Their advantages and drawbacks are investigated on the commercial turbine design software-bladed and compared through detailed simulation results. Finally, we demonstrate some simulation results and differences between the KF-based estimator and the EKF-based one.
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页码:1089 / 1096
页数:7
相关论文
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