Stochastic model predictive control: Uncertainty impact on wind farm power tracking

S. Boersma, B. M. Doekemeijer, T. Keviczky, Jan-Willem van Wingerden

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

1 Citation (Scopus)

Abstract

Active power control for wind farms is needed to provide ancillary services. One of these services is to track a power reference signal with a wind farm by dynamically de- and uprating the turbines. Due to the stochastic nature of the wind, it is necessary to take this stochastic behavior into account when evaluating control signals. In this paper we present a closed-loop stochastic wind farm controller that evaluates thrust coefficients providing power tracking under uncertain wind speed measurements. The controller is evaluated in a high-fidelity wind farm model simulating a 9-turbine wind farm to demonstrate the stochastic controller under different uncertainty levels on the wind speed measurement and different controller settings. Results illustrate that a stochastic controller provides better tracking performance with respect to its deterministic variant.

Original languageEnglish
Title of host publicationProceedings of the 2019 American Control Conference (ACC 2019)
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages4167-4172
ISBN (Electronic)978-1-5386-7926-5
DOIs
Publication statusPublished - 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: 10 Jul 201912 Jul 2019

Conference

Conference2019 American Control Conference, ACC 2019
CountryUnited States
CityPhiladelphia
Period10/07/1912/07/19

Fingerprint

Dive into the research topics of 'Stochastic model predictive control: Uncertainty impact on wind farm power tracking'. Together they form a unique fingerprint.

Cite this