Sparse-Sensor Placement for Wind Farm Control

Kathryn Johnson; Paul Fleming; Christopher Bay; Timothy Taylor; Lucy Pao; Katherine Dykes

doi:10.1088/1742-6596/1037/3/032019

Sparse-Sensor Placement for Wind Farm Control

Kathryn Johnson, Paul Fleming, Christopher Bay, Timothy Taylor, Lucy Pao, Katherine Dykes

National Wind Technology Center

Research output: Contribution to journal › Article › peer-review

15 Scopus Citations

Abstract

The objective of this paper is to incorporate sparse sensor data to improve flow-field estimates in a wind farm, which can then be used to perform better online wind farm optimization and control. A sparse-sensing algorithm is used to determine the optimal locations of sensors to improve the overall estimation precision of the flow field within the wind farm. This algorithm takes advantage of the dominant atmospheric structures in a wind farm to reconstruct the flow field from point measurements in the field. These measurements, in their optimal locations, have the ability to improve the observability of a wind farm and thus provide faster, more accurate, state estimation.

Original language	American English
Article number	Article No. 032019
Number of pages	10
Journal	Journal of Physics: Conference Series
Volume	1037
Issue number	3
DOIs	https://doi.org/10.1088/1742-6596/1037/3/032019 https://doi.org/10.1088/1742-6596/1037/3/032019
State	Published - 19 Jun 2018
Event	7th Science of Making Torque from Wind, TORQUE 2018 - Milan, Italy Duration: 20 Jun 2018 → 22 Jun 2018

Bibliographical note

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

NREL Publication Number

NREL/JA-5000-71428

Keywords

sensors
sparse sensing
wind energy
wind farm
wind plant controls

Access to Document

https://www.nrel.gov/docs/fy18osti/71428.pdf

Cite this

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title = "Sparse-Sensor Placement for Wind Farm Control",

abstract = "The objective of this paper is to incorporate sparse sensor data to improve flow-field estimates in a wind farm, which can then be used to perform better online wind farm optimization and control. A sparse-sensing algorithm is used to determine the optimal locations of sensors to improve the overall estimation precision of the flow field within the wind farm. This algorithm takes advantage of the dominant atmospheric structures in a wind farm to reconstruct the flow field from point measurements in the field. These measurements, in their optimal locations, have the ability to improve the observability of a wind farm and thus provide faster, more accurate, state estimation.",

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author = "Kathryn Johnson and Paul Fleming and Christopher Bay and Timothy Taylor and Lucy Pao and Katherine Dykes",

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AU - Johnson, Kathryn

AU - Fleming, Paul

AU - Bay, Christopher

AU - Taylor, Timothy

AU - Pao, Lucy

AU - Dykes, Katherine

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2018/6/19

Y1 - 2018/6/19

N2 - The objective of this paper is to incorporate sparse sensor data to improve flow-field estimates in a wind farm, which can then be used to perform better online wind farm optimization and control. A sparse-sensing algorithm is used to determine the optimal locations of sensors to improve the overall estimation precision of the flow field within the wind farm. This algorithm takes advantage of the dominant atmospheric structures in a wind farm to reconstruct the flow field from point measurements in the field. These measurements, in their optimal locations, have the ability to improve the observability of a wind farm and thus provide faster, more accurate, state estimation.

AB - The objective of this paper is to incorporate sparse sensor data to improve flow-field estimates in a wind farm, which can then be used to perform better online wind farm optimization and control. A sparse-sensing algorithm is used to determine the optimal locations of sensors to improve the overall estimation precision of the flow field within the wind farm. This algorithm takes advantage of the dominant atmospheric structures in a wind farm to reconstruct the flow field from point measurements in the field. These measurements, in their optimal locations, have the ability to improve the observability of a wind farm and thus provide faster, more accurate, state estimation.

KW - sensors

KW - sparse sensing

KW - wind energy

KW - wind farm

KW - wind plant controls

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DO - 10.1088/1742-6596/1037/3/032019

M3 - Article

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SN - 1742-6588

VL - 1037

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 3

M1 - Article No. 032019

T2 - 7th Science of Making Torque from Wind, TORQUE 2018

Y2 - 20 June 2018 through 22 June 2018

ER -

Sparse-Sensor Placement for Wind Farm Control

Abstract

Bibliographical note

NREL Publication Number

Keywords

Access to Document

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