Reducing aerodynamic load fluctuation in wind turbines using iterative learning control laws designed using reduced order models of the flow
Reducing aerodynamic load fluctuation in wind turbines using iterative learning control laws designed using reduced order models of the flow
Developments in actuators and sensors have led to considerable interest in their use for aerodynamic load control for wind turbines, thereby increasing power extraction efficiency, including economic competitiveness against other sources of alternative energy. In particular, the route is to embed smart devices into the rotor blades and use them in combination with active control to modify the blade section aerodynamics, aiming to minimize lift fluctuations due to disturbances. Previous research has shown that iterative learning control can be used in this area, starting with model-free designs and proceeding to model-based designs. This paper uses proper orthogonal decomposition to construct finite-dimensional models from the computational fluid dynamics-based representations of the defining partial differential equations. The performance of the resulting control laws is examined using the computational fluid dynamics representation of the dynamics.
4242-4247
Nowicka, Weronika N.
e1ac7b8b-e806-4bb8-8a6f-55c54c18c7e2
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Tutty, Owen R.
c9ba0b98-4790-4a72-b5b7-09c1c6e20375
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Nowicka, Weronika N.
e1ac7b8b-e806-4bb8-8a6f-55c54c18c7e2
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Tutty, Owen R.
c9ba0b98-4790-4a72-b5b7-09c1c6e20375
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Nowicka, Weronika N., Chu, Bing, Tutty, Owen R. and Rogers, Eric
(2022)
Reducing aerodynamic load fluctuation in wind turbines using iterative learning control laws designed using reduced order models of the flow.
In 2022 American Control Conference, (ACC).
vol. 2022-June,
IEEE.
.
(doi:10.23919/ACC53348.2022.9867215).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Developments in actuators and sensors have led to considerable interest in their use for aerodynamic load control for wind turbines, thereby increasing power extraction efficiency, including economic competitiveness against other sources of alternative energy. In particular, the route is to embed smart devices into the rotor blades and use them in combination with active control to modify the blade section aerodynamics, aiming to minimize lift fluctuations due to disturbances. Previous research has shown that iterative learning control can be used in this area, starting with model-free designs and proceeding to model-based designs. This paper uses proper orthogonal decomposition to construct finite-dimensional models from the computational fluid dynamics-based representations of the defining partial differential equations. The performance of the resulting control laws is examined using the computational fluid dynamics representation of the dynamics.
Text
Reducing_Aerodynamic_Load_Fluctuation_in_Wind_Turbines_using_Iterative_Learning_Control_laws_Designed_Using_Reduced_Order_Models_of_the_Flow
- Version of Record
Restricted to Repository staff only
Request a copy
More information
e-pub ahead of print date: 8 June 2022
Additional Information:
Publisher Copyright:
© 2022 American Automatic Control Council.
Venue - Dates:
2022 American Control Conference, ACC 2022, , Atlanta, United States, 2022-06-08 - 2022-06-10
Identifiers
Local EPrints ID: 471614
URI: http://eprints.soton.ac.uk/id/eprint/471614
ISSN: 0743-1619
PURE UUID: eab5f7d7-2e5b-4f89-b78e-7f145fece84e
Catalogue record
Date deposited: 14 Nov 2022 18:13
Last modified: 17 Mar 2024 03:28
Export record
Altmetrics
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
