Demo Data from EDBT 2026 / reviewers in the wild / expert
Kurt S. Hansen
dblp:128/2787
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2013
0000-0001-7109-3855ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 87% Environmental and earth informatics · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Energy systems and smart grids › renewable energy
wind energy |
0.2 | 1 | 2013 | Meteorological Controls on Wind Turbine Wakes · Proc. IEEE 2013 |
Environmental and earth informatics
meteorology |
0.0 | 1 | 2013 | Meteorological Controls on Wind Turbine Wakes · Proc. IEEE 2013 |
Methods — techniques the papers use, named apart from their topics
data analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Meteorological Controls on Wind Turbine WakesabstractThe primary control on the magnitude of the power losses induced by wind turbine wakes in large wind farms is the hub-height wind speed via its link to the turbine thrust coefficient. Hence, at low to moderate wind speeds (between cut-in and rated turbine wind speeds) when the thrust coefficient is high, wake losses are proportionally larger and decrease to be virtually undetectable at wind speeds above rated wind speeds. Wind direction is also critical. Not only does it determine the effective spacing between turbines but also the wind speed distribution is primarily determined by synoptic forcing and typically has a predominant direction from which wind speeds tend to be higher (from southwest for much of the central United States and northern Europe). Two other interlinked variables, turbulence intensity (TI), and atmospheric stability also dictate wake losses. Quantifying, understanding, modeling, and predicting this complex and interdependent system is therefore critical to understanding and modeling wind farm power losses due to wakes, and to optimizing wind farm layout. This paper quantifies the impact of these variables on the power loss due to wakes using data from the large offshore wind farms located at Horns Rev and Nysted in Denmark. Rebecca J. Barthelmie, Kurt S. Hansen, Sara C. Pryor |
Proc. IEEE | 2 |