Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide

Saravanan Bhaskaran, Amrit Shankar Verma (), Andrew J. Goupee, Subhamoy Bhattacharya, Amir R. Nejad and Wei Shi
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Saravanan Bhaskaran: Department of Mechanical Engineering, University of Maine, Orono, ME 04473, USA
Amrit Shankar Verma: Department of Mechanical Engineering, University of Maine, Orono, ME 04473, USA
Andrew J. Goupee: Department of Mechanical Engineering, University of Maine, Orono, ME 04473, USA
Subhamoy Bhattacharya: Department of Civil and Environmental Engineering, University of Surrey, Guildford GU2 7XH, UK
Amir R. Nejad: Department of Marine Technology, Norwegian University of Science and Technology, 7491 Trondheim, Norway
Wei Shi: Deepwater Engineering Research Center, Dalian University of Technology, Dalian 116024, China

Energies, 2023, vol. 16, issue 19, 1-26

Abstract: With 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.

Keywords: offshore wind turbines; extreme value analysis; block maxima; peaks-over-threshold (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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