Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Abstract

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

Comparative Analysis of Environmental Contour Approaches to Estimating Extreme Waves for Offshore Installations for the Baltic Sea and the North Sea

“…This is an indication that the bulk of the data is represented reasonably well by the model at these two locations, whereas the extremes (high H s ) are not, which is a known issue for this hindcast data [36]. As noted in Section 1, Neary et al [30] reported, on the other hand, a hindcast underestimation of sea states with high H s along the environmental contours for their study sites. However, differences in wave climate, hindcast models, and contour method makes a direct comparison difficult.…”

“…Such considerations further highlight the importance of understanding and characterizing extreme sea states. A recent publication by Neary et al [30] compared environmental contours for buoy observations and different hindcast data sets for several locations on the West Coast and East Coast of USA with a focus on extreme sea states for WEC applications. They employed the I-FORM with PCA to estimate environmental contours, and found that the model data generally lead to smaller contours compared with those from observations and that estimates of extreme sea states with high H s were underestimated by the models.…”

Joint Modelling of Wave Energy Flux and Wave Direction

“…In the framework of wave resource assessment studies, data validation, variability and uncertainty measures, and analysis of extreme waves should also be included. See, e.g., [4,[19][20][21][22][23][24].…”

Self-Adaptive Global-Best Harmony Search Algorithm-Based Airflow Control of a Wells-Turbine-Based Oscillating-Water Column

“…The three major obstacles of the industrialization of WECs are associated with reliability, efficiency, and economic viability, but extensive efforts are being spent on the improvement of these aspects [8]. Reliability is achieved by refining the WEC designs and optimizing control strategies [9,10], while economic viability and efficiency require the optimal selection of the site, the system, and materials in order to reduce costs and environmental impact while guaranteeing maximum energy absorption [11,12].…”