Wave power statistics for individual waves

Myrhaug, Dag; Leira, Bernt J.; Holm, Håvard Heitlo

doi:10.1016/j.apor.2009.07.001

Cited by 9 publications

(3 citation statements)

References 14 publications

(14 reference statements)

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“…For instance, parametric bivariate distributions, conditional-distribution approach, kernel-based models, and copula-based models as well, provide alternative probabilistic description of these variables; see, e.g., [27,[30][31][32]. Furthermore, [33,34] have provided distributions for the joint description of wave power-wave height and wave power-wave period for individual waves, an approach that is complementary in long-term analysis of the wave climate at a particular site.…”

Section: Introductionmentioning

confidence: 99%

Joint Modelling of Wave Energy Flux and Wave Direction

Soukissian

Karathanasi

2021

Processes

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In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.

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Section: Introductionmentioning

confidence: 99%

Joint Modelling of Wave Energy Flux and Wave Direction

Soukissian

Karathanasi

2021

Processes

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“…Sea states are characterized by the significant wave height H s and the mean zero-crossing wave period T z (or the spectral peak period T p ), and the individual random waves within a sea state are characterized by the wave height H and the wave period T. Thus, the statistical properties of wave power for sea states and for individual waves are of interest; also jointly with the corresponding sea state wave parameters and the individual wave parameters. Wave power statistics for sea states based on wave statistics have been provided by, for example, Ertekin and Xu, 4 Pontes, 5 Beels et al, 6 Kerbiriou et al, 7 and Myrhaug et al 8 Previous works on wave power statistics for individual waves based on wave statistics are Smith et al, 9 Venugopal and Smith, 1 Myrhaug et al, 10 and Leira and Myrhaug. 11 Izadparast and Niedzwecki 12 applied joint statistics of H and T and provided an example of how to evaluate the longterm potential of wave power.…”

Section: Introductionmentioning

confidence: 99%

“…Ertekin and Xu 4 , Pontes 5 , Beels et al 6 , Kerbiriou et al 7 and Myrhaug et al 8 . Previous works on wave power statistics for individual waves based on waves statistics are Smith et al 9 , Venugopal and Smith 1 , Myrhaug et al 10 , and Leira and Myrhaug 11 . Izadparast and Niedzwechi 12 applied joint statistics of H and T and provided an example of how to evaluate the long-term potential of wave power.…”

Section: Introductionmentioning

confidence: 99%

Estimation of wave power in shallow water using deep water wind and wave statistics

Myrhaug

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The article addresses how the wave power in shallow water can be estimated based on available wind and wave statistics for a deep water ocean area. The average statistical properties of the wave power in shallow water expressed in terms of the mean value and the standard deviation are presented. Results are exemplified by using long-term wind and wave statistics from the same ocean area in the Northern North Sea. Overall, it appears that there is agreement between the results based on these inputs from wind and wave statistics. The presented analytical method should be useful for making preliminary estimates of the wave power potential in shallow water using either available deep water wind statistics or deep water wave statistics, which enhances the possibilities for assessing further the wave power potential in, for example, near-coastal zones.

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Statistical investigation of wave power potential in the North Aegean Sea

Pinar

Alkanat

2023

Clean Techn Environ Policy

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Wave power statistics for individual waves

Cited by 9 publications

References 14 publications

Joint Modelling of Wave Energy Flux and Wave Direction

Joint Modelling of Wave Energy Flux and Wave Direction

Estimation of wave power in shallow water using deep water wind and wave statistics

Statistical investigation of wave power potential in the North Aegean Sea

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