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

共 50 条

[11] Data-driven optimization for wind farm siting
Arrieta-Prieto, Mario E.
Schell, Kristen R.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2024, 155
[12] Data-driven wind turbine wake modeling via probabilistic machine learning
S. Ashwin Renganathan
Romit Maulik
Stefano Letizia
Giacomo Valerio Iungo
Neural Computing and Applications, 2022, 34 : 6171 - 6186
[13] Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling
Li, Xueping
Huang, Sheng
Qu, Yinpeng
Luo, Derong
Peng, Hanzhi
Wu, Qiuwei
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2024, 161
[14] Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Bempedelis, Nikolaos
Gori, Filippo
Wynn, Andrew
Laizet, Sylvain
Magri, Luca
WIND ENERGY SCIENCE, 2024, 9 (04) : 869 - 882
[15] Data-driven lumped dynamic modelling of wind farm frequency regulation characteristics
Li, Shaolin
Lu, Jianmou
Qin, Shiyao
Hu, Yang
Fang, Fang
IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS, 2022, 7 (03) : 147 - 156
[16] Efficient Stochastic Wake Modeling for Wind Farm Control
Taylor, Tim
Johnson, Kathryn
2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2017,
[17] A Wake Modeling Paradigm for Wind Farm Design and Control
Shapiro, Carl R.
Starke, Genevieve M.
Meneveau, Charles
Gayme, Dennice F.
ENERGIES, 2019, 12 (15)
[18] Adaptive transient overvoltage control strategy for wind farm based on data-driven
Liu, Xiaofei
Zhang, Pei
IET ELECTRIC POWER APPLICATIONS, 2024, 18 (04) : 413 - 424
[19] A data-driven, cooperative wind farm control to maximize the total power production
Park, Jinkyoo
Law, Kincho H.
APPLIED ENERGY, 2016, 165 : 151 - 165
[20] Data-Driven Wind Farm Control via Multiplayer Deep Reinforcement Learning
Dong, Hongyang
Zhao, Xiaowei
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2023, 31 (03) : 1468 - 1475

← 1 2 3 4 5 →