Data-Driven Modeling & Analysis of Dynamic Wake for Wind Farm Control: A Comparison Study

被引：2

作者：

Chen, Zhenyu ^{[1
]}

Doekemeijer, Bart M. ^{[2
]}

Lin, Zhongwei ^{[1
]}

Xie, Zhen ^{[1
]}

Si, Zongming ^{[3
]}

Liu, Jizhen ^{[1
]}

Van Wingerden, Jan-Willem ^{[2
]}

机构：

[1] North China Elect Power Univ, Sch Control Comp & Engn, State Key Lab Alternate Elect Power Syst Renewabl, Beijing, Peoples R China

[2] Delft Univ Technol, Delft Ctr Syst & Control DCSC, Fac Mech Maritime & Mat Engn 3mE, Delft, Netherlands

[3] CHN Energy Shandong New Energy Co Ltd, Jinan 250099, Peoples R China

来源：

2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

Dynamic mode decomposition; Wake effect; Dynamic system; Identification; Frequency domain;

D O I：

10.1109/CAC51589.2020.9327624

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For the study of wind farm and wake effect, the steady-state wake models like FLORIS were proposed and used during wind farm operations to achieve higher wind power utilization and conversion. However, the dynamic performance of the wake should also be involved in favor of better optimizations. In this paper, a data-driven analysis and modeling method, dynamic mode decomposition (DMD), is used to construct dynamic flow model with high-fidelity flow data. Two DMD-derived models are constructed based on flow data in three-dimensional and two-dimensional spaces, respectively. The obtained models and respective (lows are compared in the time and frequency domain. Results show that both models have apparent differences in the frequency domain, but the dominant wake characteristics' consistency is maintained.

引用

页码：5326 / 5331

页数：6

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