Joint state-parameter estimation for a control-oriented LES wind farm model

B. M. Doekemeijer, S. Boersma, L. Y. Pao, J. W. Van Wingerden

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
48 Downloads (Pure)

Abstract

Wind farm control research typically relies on computationally inexpensive, surrogate models for real-time optimization. However, due to the large time delays involved, changing atmospheric conditions and tough-to-model flow and turbine dynamics, these surrogate models need constant calibration. In this paper, a novel real-time (joint state-parameter) estimation solution for a medium-fidelity dynamical wind farm model is presented. In this work, we demonstrate the estimation of the freestream wind speed, local turbulence, and local wind field in a two-turbine wind farm using exclusively turbine power measurements. The estimator employs an Ensemble Kalman filter with a low computational cost of approximately 1.0 s per timestep on a dual-core notebook CPU. This work presents an essential building block for real-time wind farm control using computationally efficient dynamical wind farm models.

Original languageEnglish
Title of host publicationJournal of Physics: Conference Series
Subtitle of host publicationThe Science of Making Torque from Wind (TORQUE 2018)
Place of PublicationBristol, UK
PublisherIOP Publishing
Number of pages8
Volume1037
DOIs
Publication statusPublished - 2018
EventTORQUE 2018: The Science of Making Torque from Wind - Milano, Italy
Duration: 20 Jun 201822 Jun 2018
http://www.torque2018.org/

Publication series

NameJournal of Physics: Conference Series
PublisherIOP Publishing Ltd.
ISSN (Print)1742-6588

Conference

ConferenceTORQUE 2018
Abbreviated titleTORQUE 2018
CountryItaly
CityMilano
Period20/06/1822/06/18
Internet address

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