Offshore renewable energy site correlated wind-wave statistics

• Published in: Probabilistic engineering mechanics Vol. 68; p. 103207
• Main Authors: Gaidai, Oleg, Wang, Fang, Wu, Yu, Xing, Yihan, Medina, Ausberto Rivera, Wang, Junlei
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.04.2022; Elsevier Science Ltd
• Subjects: Bivariate analysis; Correlation analysis; Datasets; Estimating techniques; Extreme value statistics; Extreme values; Offshore; Offshore engineering; Offshore wind speed; Propagation; Random processes; Remote sensing; Renewable energy environment; SEM-REV; TOPEX; Turbines; Wave height; Wave height statistics; Wave power; Weather stations; Wind power; Wind speed; Wind waves; Extreme value statistics; Renewable energy environment; Wave height statistics; Offshore wind speed; SEM-REV
• ISSN: 0266-8920; 1878-4275
• DOI: 10.1016/j.probengmech.2022.103207

Offshore engineering often requires estimation of extreme wind speeds and wave heights at offshore in-situ locations. This paper presents practical approach to study extreme wind speed and wave height statistics, based on available in-situ hourly wind speed and wave height maxima. The wind and wave data, studied in this paper, was obtained from numerical hind cast model, locally applied to the offshore the area that covers SEM-REV (European wind and wave energy research site) location, for the time period 2001–2010 years. To obtain accurate local wind and wave data set, accurate wave and wind measurements from in-situ monitoring tools (meteorological stations and buoys) as well as remote satellite sensing data (ENVISAT and TOPEX satellite records) were plugged into atmospheric wind and wave model. The novel bivariate correction technique based on the Average Conditional Exceedance Rate (ACER) method has been presented in brief detail. The bivariate correction method produced quite accurate extreme value predictions, efficiently utilizing available wind speeds and wave heights data set. In some practical situations it would be useful to improve accuracy of some statistical predictions, using information supplied by another synchronous highly correlated random process that has been measured for a longer time than the process of interest. In this paper the novel technique of improving correlated extreme wind speed and wave height predictions has been presented.