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On the Failure Probability of Offshore Wind Turbines in the China Coastal Waters Due to Typhoons: A Case Study Using the OC4-DeepCwind Semisubmersible
- Source :
- IEEE Transactions on Sustainable Energy. 10:522-532
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- The paper introduces a set of modeling methods to predict the failure probability of offshore wind turbines in the China coastal waters. In detail, a series of full-set three-dimensional meteorology simulations are conducted using the Weather Research and Forecast model to provide the extreme wind fields, while the extreme wave fields are predicted according to the conditional probability model. After the extreme wind and wave fields are prepared, the ultimate loads on the three critical parts of an OC4-DeepCwind semisubmersible wind turbine are simulated using the fatigue, aerodynamics, structures, and turbulence code, which in turn leads to the prediction of the failure probability. It has been found that the OC4-DeepCwind semisubmersible constructed in the Taiwan Strait has higher chance to fail under typhoon conditions than in other parts of the China coastal waters, showing a maximum failure probability of 0.6. In addition, the failure probabilities in the South and East China Sea are in the range of 0.3–0.45. The probability densities of the ultimate loads show, in some cases, a bimodal shape, which indicates that the loads as well as the failure probability are not in a simple linear relationship with the extreme wind speeds and wave heights/periods.
- Subjects :
- Wind power
Meteorology
Renewable Energy, Sustainability and the Environment
business.industry
020209 energy
Conditional probability
020101 civil engineering
02 engineering and technology
Aerodynamics
Wind speed
0201 civil engineering
Offshore wind power
Typhoon
0202 electrical engineering, electronic engineering, information engineering
Range (statistics)
Environmental science
Tropical cyclone
business
Subjects
Details
- ISSN :
- 19493037 and 19493029
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Sustainable Energy
- Accession number :
- edsair.doi...........fd5be3c5ac8d7a160730044d81b3839e