Abstract
| Original language | English |
|---|---|
| Publication date | 2019 |
| Number of pages | 2 |
| Publication status | Published - 2019 |
| Event | NAWEA/WINDTECH 2019 - University of Massachusetts , Amherst , United States Duration: 14 Oct 2019 → 16 Oct 2019 https://www.naweawindtech2019.org |
Conference
| Conference | NAWEA/WINDTECH 2019 |
|---|---|
| Location | University of Massachusetts |
| Country | United States |
| City | Amherst |
| Period | 14/10/2019 → 16/10/2019 |
| Internet address |
Keywords
- Extreme events
- Design load cases
- 50-year return values
- IEC standard
- Validation
Cite this
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Can we predict short-term extreme conditions from 10-minute data only? / Hannesdóttir, Ásta; Larsen, Gunner Chr.
2019. Abstract from NAWEA/WINDTECH 2019, Amherst , United States.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
TY - ABST
T1 - Can we predict short-term extreme conditions from 10-minute data only?
AU - Hannesdóttir, Ásta
AU - Larsen, Gunner Chr.
PY - 2019
Y1 - 2019
N2 - In this study we validate a model that predicts extreme wind load conditions relevant for wind turbine design. The input parameters for the model consist of 10-minute wind data, rather than high-frequency measurements that are less available. The model generally predicts lower extreme values than prescribed in the IEC wind turbine safety standard and is therefore of great interest to wind turbine manufacturers. This is the first time this extreme model is validated and the accuracy of the model predictions is estimated.
AB - In this study we validate a model that predicts extreme wind load conditions relevant for wind turbine design. The input parameters for the model consist of 10-minute wind data, rather than high-frequency measurements that are less available. The model generally predicts lower extreme values than prescribed in the IEC wind turbine safety standard and is therefore of great interest to wind turbine manufacturers. This is the first time this extreme model is validated and the accuracy of the model predictions is estimated.
KW - Extreme events
KW - Design load cases
KW - 50-year return values
KW - IEC standard
KW - Validation
M3 - Conference abstract for conference
ER -