Data-driven parameter calibration in wake models

Bingjie Liu; Eunshin Byon; Matthew Plumlee

doi:10.2514/6.2018-2017

Data-driven parameter calibration in wake models

Bingjie Liu, Eunshin Byon, Matthew Plumlee

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Physical interactions among wind turbines, called wake effects, are known to be one of the significant factors that affect power generation performance in wind power systems. Among several wake modeling approaches, physics-based engineering models, such as Jensen’s model, have been widely used due to their computational tractability. Although substantial efforts have been made to improve the accuracy of engineering wake models, few studies suggest calibrating the model parameters in the literature. We propose a new data-driven calibration approach for adjusting the model parameters using real operational data.

Original language	English (US)
Title of host publication	Wind Energy Symposium
Publisher	American Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)	9781624105227
DOIs	https://doi.org/10.2514/6.2018-2017
State	Published - 2018
Event	Wind Energy Symposium, 2018 - Kissimmee, United States Duration: Jan 8 2018 → Jan 12 2018

Publication series

Name	Wind Energy Symposium, 2018

Other

Other	Wind Energy Symposium, 2018
Country/Territory	United States
City	Kissimmee
Period	1/8/18 → 1/12/18

Funding

This work was supported by the National Science Foundation (Grant No. CMMI-1362513, CMMI-1536924, and IIS-1741166) and the University of Michigan MCubed Grant.

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Mechanical Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2514/6.2018-2017

Cite this

@inproceedings{8579cfb207e34f25be49c60e2eefb078,

title = "Data-driven parameter calibration in wake models",

abstract = "Physical interactions among wind turbines, called wake effects, are known to be one of the significant factors that affect power generation performance in wind power systems. Among several wake modeling approaches, physics-based engineering models, such as Jensen{\textquoteright}s model, have been widely used due to their computational tractability. Although substantial efforts have been made to improve the accuracy of engineering wake models, few studies suggest calibrating the model parameters in the literature. We propose a new data-driven calibration approach for adjusting the model parameters using real operational data.",

author = "Bingjie Liu and Eunshin Byon and Matthew Plumlee",

note = "Funding Information: This work was supported by the National Science Foundation (Grant No. CMMI-1362513, CMMI-1536924, and IIS-1741166) and the University of Michigan MCubed Grant. Publisher Copyright: {\textcopyright} 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.; Wind Energy Symposium, 2018 ; Conference date: 08-01-2018 Through 12-01-2018",

year = "2018",

doi = "10.2514/6.2018-2017",

language = "English (US)",

isbn = "9781624105227",

series = "Wind Energy Symposium, 2018",

publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",

booktitle = "Wind Energy Symposium",

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TY - GEN

T1 - Data-driven parameter calibration in wake models

AU - Liu, Bingjie

AU - Byon, Eunshin

AU - Plumlee, Matthew

N1 - Funding Information: This work was supported by the National Science Foundation (Grant No. CMMI-1362513, CMMI-1536924, and IIS-1741166) and the University of Michigan MCubed Grant. Publisher Copyright: © 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

PY - 2018

Y1 - 2018

N2 - Physical interactions among wind turbines, called wake effects, are known to be one of the significant factors that affect power generation performance in wind power systems. Among several wake modeling approaches, physics-based engineering models, such as Jensen’s model, have been widely used due to their computational tractability. Although substantial efforts have been made to improve the accuracy of engineering wake models, few studies suggest calibrating the model parameters in the literature. We propose a new data-driven calibration approach for adjusting the model parameters using real operational data.

AB - Physical interactions among wind turbines, called wake effects, are known to be one of the significant factors that affect power generation performance in wind power systems. Among several wake modeling approaches, physics-based engineering models, such as Jensen’s model, have been widely used due to their computational tractability. Although substantial efforts have been made to improve the accuracy of engineering wake models, few studies suggest calibrating the model parameters in the literature. We propose a new data-driven calibration approach for adjusting the model parameters using real operational data.

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U2 - 10.2514/6.2018-2017

DO - 10.2514/6.2018-2017

M3 - Conference contribution

AN - SCOPUS:85141641603

SN - 9781624105227

T3 - Wind Energy Symposium, 2018

BT - Wind Energy Symposium

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

T2 - Wind Energy Symposium, 2018

Y2 - 8 January 2018 through 12 January 2018

ER -

Data-driven parameter calibration in wake models

Abstract

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Funding

ASJC Scopus subject areas

UN SDGs

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