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Data-driven modelling of wind farm flow control strategies

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven modelling of wind farm flow control strategiesby Nassir Cassamo, Jan-Willem van Wingerden.
Author:	Cassamo, Nassir.
other author:	Wingerden, Jan-Willen van.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xiv, 156 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Wind power plantsMathematical models.
Online resource:	https://doi.org/10.1007/978-3-031-84116-3
ISBN:	9783031841163$q(electronic bk.)

Data-driven modelling of wind farm flow control strategies
Cassamo, Nassir.

Data-driven modelling of wind farm flow control strategies[electronic resource] /by Nassir Cassamo, Jan-Willem van Wingerden. - Cham :Springer Nature Switzerland :2025. - xiv, 156 p. :ill., digital ;24 cm.

Introduction -- Wind Turbine and Wind Farm Control -- Data-Driven Modelling of Wind Farm Control Strategies -- Methodology for Modelling Wind Farm Control Strategies -- Data Driven Modelling of Wake Redirection Control -- Data-Driven Modelling of Axial Induction Control -- Advanced Topics -- Conclusions.

This book presents data-driven algorithms used in the context of wind farm modelling and exploits their relation with concepts from non-linear dynamical system theory. The algortihms include Input Output Dynamic Mode Decomposition and their combination with the Koopman Operator theory. The latter improves on modelling and analysis of the aerodynamic interaction between wind turbines in wind farms and assists in uncovering insights into the existing dynamics and improves models accuracy. The authors introduce the topic of wind farm flow control, illustrating current strategies devised to overcome power losses in wind plants due to the aerodynamic interaction between turbines. Although controlling wind farms as a whole is becoming increasingly important, the high dimensions and governing non-linear dynamics inherent of wind farm systems make the design of numerical optimal controllers computationally expensive. This book describes a possible pathway to circumvent this challenge through reduced order models that can embed the existing non-linearities. The authors make use of high fidelity open-source simulation datasets and developed algorithms to fully show the potential of this approach using visual results. The reader is motivated to use the datasets and algorithms and exploit the potential of the reduced order models. Provides a thorough review of wind farm control strategies. Illustrates wind farm control strategies with data from simulations and using reduced order models. Maximizes reader understanding of state of the art data-driven algorithms applied to wind farm control.

ISBN: 9783031841163$q(electronic bk.)

Standard No.: 10.1007/978-3-031-84116-3doiSubjects--Topical Terms:

993076
Wind power plants
--Mathematical models.

LC Class. No.: TK1541

Dewey Class. No.: 621.312136

Data-driven modelling of wind farm flow control strategies
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505 0 $a Introduction -- Wind Turbine and Wind Farm Control -- Data-Driven Modelling of Wind Farm Control Strategies -- Methodology for Modelling Wind Farm Control Strategies -- Data Driven Modelling of Wake Redirection Control -- Data-Driven Modelling of Axial Induction Control -- Advanced Topics -- Conclusions.
520 $a This book presents data-driven algorithms used in the context of wind farm modelling and exploits their relation with concepts from non-linear dynamical system theory. The algortihms include Input Output Dynamic Mode Decomposition and their combination with the Koopman Operator theory. The latter improves on modelling and analysis of the aerodynamic interaction between wind turbines in wind farms and assists in uncovering insights into the existing dynamics and improves models accuracy. The authors introduce the topic of wind farm flow control, illustrating current strategies devised to overcome power losses in wind plants due to the aerodynamic interaction between turbines. Although controlling wind farms as a whole is becoming increasingly important, the high dimensions and governing non-linear dynamics inherent of wind farm systems make the design of numerical optimal controllers computationally expensive. This book describes a possible pathway to circumvent this challenge through reduced order models that can embed the existing non-linearities. The authors make use of high fidelity open-source simulation datasets and developed algorithms to fully show the potential of this approach using visual results. The reader is motivated to use the datasets and algorithms and exploit the potential of the reduced order models. Provides a thorough review of wind farm control strategies. Illustrates wind farm control strategies with data from simulations and using reduced order models. Maximizes reader understanding of state of the art data-driven algorithms applied to wind farm control.
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