当前位置: X-MOL 学术Int. J. Green Energy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Machine learning based model linearization of a wind turbine for power regulation
International Journal of Green Energy ( IF 3.1 ) Pub Date : 2021-09-14 , DOI: 10.1080/15435075.2021.1884865
Peyman Sindareh Esfahani 1 , Jeffrey Kurt Pieper 1
Affiliation  

ABSTRACT

Wind turbine systems exhibit highly nonlinear dynamics influenced by the aerodynamic torque induced in the wind turbine blades and thrust force on the turbine structure due to the wind flow. This paper presents a system identification approach to approximate the nonlinear wind turbine model. A clustering-based piecewise affine system identification technique is utilized to construct an affine multiple-model that is valid for the power regulation region of a wind turbine. A comprehensive study is performed to validate the accuracy and performance of the developed model. The piecewise affine model identified in this paper can be widely used for advanced control systems design and the security assessment of the power grid.



中文翻译:

基于机器学习的风力涡轮机模型线性化用于功率调节

摘要

风力涡轮机系统表现出高度非线性动力学,受风力涡轮机叶片中引起的空气动力扭矩和由于风流作用在涡轮机结构上的推力的影响。本文提出了一种近似非线性风力涡轮机模型的系统辨识方法。基于聚类的分段仿射系统辨识技术被用来构建一个对风力涡轮机功率调节区域有效的仿射多重模型。进行了全面的研究以验证开发模型的准确性和性能。本文确定的分段仿射模型可广泛用于先进控制系统设计和电网安全评估。

更新日期:2021-11-01
down
wechat
bug