Data-Driven LIDAR Feedforward Predictive Wind Turbine Control

Rogier Dinkla; Tom Oomen; Jan Willem Van Wingerden; Sebastiaan P. Mulders

doi:10.1109/CCTA54093.2023.10252439

Data-Driven LIDAR Feedforward Predictive Wind Turbine Control

Rogier Dinkla^*, Tom Oomen, Jan Willem Van Wingerden, Sebastiaan P. Mulders

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

14 Downloads (Pure)

Abstract

Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Publisher	IEEE
Pages	559-565
ISBN (Electronic)	979-8-3503-3544-6
DOIs	https://doi.org/10.1109/CCTA54093.2023.10252439
Publication status	Published - 2023
Event	2023 IEEE Conference on Control Technology and Applications, CCTA 2023 - Bridgetown, Barbados Duration: 16 Aug 2023 → 18 Aug 2023

Conference

Conference	2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Country/Territory	Barbados
City	Bridgetown
Period	16/08/23 → 18/08/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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Data-Driven_LIDAR_Feedforward_Predictive_Wind_Turbine_ControlFinal published version, 1.77 MB

Cite this

@inproceedings{437fafea537248d885191387f876fafb,

title = "Data-Driven LIDAR Feedforward Predictive Wind Turbine Control",

abstract = "Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.",

author = "Rogier Dinkla and Tom Oomen and Wingerden, {Jan Willem Van} and Mulders, {Sebastiaan P.}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2023 IEEE Conference on Control Technology and Applications, CCTA 2023 ; Conference date: 16-08-2023 Through 18-08-2023",

year = "2023",

doi = "10.1109/CCTA54093.2023.10252439",

language = "English",

pages = "559--565",

booktitle = "Proceedings of the 2023 IEEE Conference on Control Technology and Applications, CCTA 2023",

publisher = "IEEE",

address = "United States",

}

Dinkla, R , Oomen, T , Wingerden, JWV & Mulders, SP 2023, Data-Driven LIDAR Feedforward Predictive Wind Turbine Control. in Proceedings of the 2023 IEEE Conference on Control Technology and Applications, CCTA 2023. IEEE, pp. 559-565, 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Bridgetown, Barbados, 16/08/23. https://doi.org/10.1109/CCTA54093.2023.10252439

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T1 - Data-Driven LIDAR Feedforward Predictive Wind Turbine Control

AU - Dinkla, Rogier

AU - Oomen, Tom

AU - Wingerden, Jan Willem Van

AU - Mulders, Sebastiaan P.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.

AB - Light Detection and Ranging (LIDAR)-assisted Model Predictive Control (MPC) for wind turbine control has received much attention for its ability to incorporate future wind speed disturbance information in a receding horizon optimal control problem. However, the growth of wind turbine sizes results in increasing system complexity and system interactions, and complicates the design of model-based controllers like MPC. Together with increasing data availability, this obstacle motivates the use of direct data-driven predictive control approaches like Subspace Predictive Control (SPC). An SPC implementation is developed that both does not suffer from traditional, potentially detrimental closed-loop identification bias and incorporates past and future (not necessarily periodic) disturbance information. Simulations of the presented method for above-rated wind turbine rotor speed regulation using pitch control demonstrate the capabilities of the data-driven SPC algorithm for increasing degrees of wind speed disturbance information in the developed framework.

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M3 - Conference contribution

AN - SCOPUS:85173853868

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EP - 565

BT - Proceedings of the 2023 IEEE Conference on Control Technology and Applications, CCTA 2023

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Y2 - 16 August 2023 through 18 August 2023

ER -

Data-Driven LIDAR Feedforward Predictive Wind Turbine Control

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