A statistical methodology for the estimation of extreme wave conditions for offshore renewable applications

Abstract

keywords:Significant wave height The 50-year return significant wave height Spectral correction a b s t r a c t Accurate estimation of extreme wave conditions is critical for offshore renewable energy activities and applications. The use of numerical wind and wave models gives a credible and convenient way of monitoring the general atmospheric and sea state conditions, especially in the absence of sufficient observational networks. However, when focusing on the study of non-frequent cases, in particular over coastal areas, increased uncertainty in the model outputs and accordingly in the reliability of the estimation of extreme waves becomes an important issue. The current study introduces a methodology to validate and post-process outputs from a high resolution numerical wave modeling system for extreme wave estimation based on the significant wave height. This approach is demonstrated through the data analysis at a relatively deep water site, FINO 1, as well as a relatively shallow water area, coastal site Horns Rev, which is located in the North Sea, west of Denmark. The post-processing targets at correcting the modeled time series of the significant wave height, in order to match the statistics of the corresponding measurements, including not only the conventional parameters such as the mean and standard deviation, but also a new parameter, the second-order spectral moment. This second-order spectral moment is essential for extreme value estimation but has so far been neglected in relevant studies. The improved model results are utilized for the estimation of the 50-year values of significant wave height as a characteristic index of extreme wave conditions. The results from the proposed methodology seem to be in a good agreement with the measurements at both the relatively deep, open water and the shallow, coastal water sites, providing a potentially useful tool for offshore renewable energy applications.

Estimation of offshore extreme wind from wind‐wave coupled modeling

“…Briefly, the wave data were collected from a buoy (“Wave Rider S”, 7.5298°E, 55.4798°N), close to the meteorological mast M2. The water depth ( D ) at the buoy site varies from 6 to 12 m, which can be considered as intermediate to shallow water according to the distribution of the ratio of water depth and the peak wave length L p . The waves were measured through the vertical acceleration of the buoy.…”

“…The water depth (D) at the buoy site varies from 6 to 12 m, which can be considered as intermediate to shallow water according to the distribution of the ratio of water depth and the peak wave length L p . 35 The waves were measured through the vertical acceleration of the buoy. From the measured one-dimensional wave power spectrum, the significant wave height H s was derived using 30-minute time series.…”

North Sea Wave Database (NSWD) and the Need for Reliable Resource Data: A 38 Year Database for Metocean and Wave Energy Assessments

“…The majority of information is based on oceanic models and larger area studies, which by default encompass the North Sea in general, but at a very coarse resolution. The wave characterisation at such large scales have been done by several authors with resulted datasets providing important information on the state of Climate Change over the region [3], return values for extreme events [7,21], and climate characterisation [22]. The studies used models that are efficient at larger domains, but have inherit limitation in terms of resolving higher resolution domains and nearshore complexities [19].…”

Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach

“…8(a) and (b) shows scatter plots for the problem of P prediction, with the ELM and SVR, respectively. Regarding the variables selected in the P prediction, we obtained the following features: [4,5,6,7,10,14,15,16,18], which correspond to variables WVHT, DPD, APD, MWD, WTMP in buoy 46025 and WVHT, DPD, APD and MWD in buoy 46042. Note that the H m0 of the neighbor buoys is a very important parameter to obtain an accurate prediction of P.…”