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dc.contributor.authorBhaskaran, Saravanan
dc.contributor.authorVerma, Amrit Shankar
dc.contributor.authorGoupee, Andrew J.
dc.contributor.authorBhattacharya, Subhamoy
dc.contributor.authorNejad, Amir R.
dc.contributor.authorShi, Wei
dc.identifier.citationEnergies. 2023, 16 (19), .en_US
dc.description.abstractWith the ongoing global drive towards renewable energy, several potential offshore wind energy lease areas worldwide have come into focus. This study aims to estimate the extreme wind and wave conditions across several newly designated offshore wind lease sites spanning six continents that are crucial for risk assessment and the design of offshore wind turbines. Firstly, the raw data of wind speeds and wave heights prevailing in these different lease areas were obtained. Following this, an in-depth extreme value analysis was performed over different return periods. Two principal methodologies were applied for this comparative study: the block-maxima and the peaks-over-threshold (POT) approaches. Various statistical techniques, including the Gumbel method of moments, Gumbel maximum likelihood, Gumbel least-squares, and the three-parameter GEV, were employed under the block-maxima approach to obtain the distribution parameters. The threshold for the POT approach was defined using the mean residual life method, and the distribution parameters were obtained using the maximum likelihood method. The Gumbel least-squares method emerged as the most conservative estimator of extreme values in the majority of cases, while the POT approach generally yielded lower extreme values compared to the block-maxima approach. However, the results from the POT approach showed large variations based on the selected threshold. This comprehensive study’s findings will provide valuable input for the efficient planning, design, and construction of future offshore wind farms.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleComparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwideen_US
dc.title.alternativeComparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwideen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US

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