Model predictive active power control of waked ind farms

Mehdi Vali, Vlaho Petrović, Sjoerd Boersma, Jan Willem Van Wingerden, L.Y Pao, Martin Kuhn

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

7 Citations (Scopus)

Abstract

In this paper, an adjoint-based model predictive control (AMPC) is proposed in order to provide active power control (APC) services of wind farms, even in the presence of problematic wake interactions. The control objective is defined to minimize wind farm power reference tracking error. The non-unique optimal distribution of wind turbine power references is a resulting by-product which can be very informative for other wind farm control methods. The developed predictive controller employs a medium-fidelity 2D dynamic wind farm model to predict wake interactions at hub-height of wind turbines in advance. An adjoint approach as a computationally efficient tool is utilized to compute the gradient for such a large-scale system. The axial induction factor of each wind turbine is considered here as a control variable to influence the overall performance of a wind farm by taking the wake interactions of the wind turbines into account. The performance of the AMPC-based APC is examined for a layout of a 2×3 wind farm in a wake condition through simulation studies. The results show the effectiveness of the proposed approach and introduce some potential studies to improve and extend its performance.

Original languageEnglish
Title of host publicationProceedings of the 2018 Annual American Control Conference (ACC 2018)
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages707-714
ISBN (Print)9781538654286
DOIs
Publication statusPublished - 2018
Event2018 Annual American Control Conference - Milwauke, United States
Duration: 27 Jun 201829 Jun 2018

Conference

Conference2018 Annual American Control Conference
Abbreviated titleACC 2018
CountryUnited States
CityMilwauke
Period27/06/1829/06/18

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