3046905
doi
10.1088/1742-6596/753/5/052015
oai:zenodo.org:3046905
van Wingerden, Jan-Willem
Delft University of Technology
Boersma, Sjoerd
Delft University of Technology
Pao, Lucy Y.
University of Colorado Boulder
Enhanced Kalman Filtering for a 2D CFD NS Wind Farm Flow Model
Doekemeijer, Bart M.
Delft University of Technology
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
wind farm
wind farm control
kalman filtering
state estimation
<p>Wind turbines are often grouped together for financial reasons, but due to wake development this usually results in decreased turbine lifetimes and power capture, and thereby an increased levelized cost of energy (LCOE). Wind farm control aims to minimize this cost by operating turbines at their optimal control settings. Most state-of-the-art control algorithms are open-loop and rely on low fidelity, static flow models. Closed-loop control relying on a dynamic model and state observer has real potential to further decrease wind's LCOE, but is often too computationally expensive for practical use. In this paper two time-efficient Kalman filter (KF) variants are outlined incorporating the medium fidelity, dynamic flow model "WindFarmSimulator" (WFSim). This model relies on a discretized set of Navier-Stokes equations in two dimensions to predict the flow in wind farms at low computational cost. The filters implemented are an Ensemble KF and an Approximate KF. Simulations in which a high fidelity simulation model represents the true wind farm show that these filters are 10-100 times faster than a regular KF with comparable or better performance, correcting for wake dynamics that are not modeled in WFSim (noticeably, wake meandering and turbine hub effects). This is a first big step towards real-time closed-loop control for wind farms.</p>
Zenodo
2016-10-05
info:eu-repo/semantics/conferencePaper
3046904
final
award_title=Closed Loop Wind Farm Control; award_number=727477; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/727477; funder_id=00k4n6c32; funder_name=European Commission;
1579541470.119277
12266495
md5:a16bcdcd5b887b27bccd385df6647d05
https://zenodo.org/records/3046905/files/paper.pdf
public