Stochastic modeling of long-term and extreme value estimation of wind and sea conditions for probabilistic reliability assessments of wave energy devices

Abstract

Wave energy power plants are expected to become one of the major future contributions to the sustainable electricity production. Optimal design of wave energy power plants is associated with modeling of physical, statistical, measurement and model uncertainties. This paper presents stochastic models for the significant wave height, the mean zero-crossing wave period and the wind speed for long-term and extreme estimations. The long-term estimation focuses on annual statistical distributions, the inter-annual variation of distribution parameters and the statistical uncertainty due to limited amount of data. The stochastic model for extreme value estimation covers annual extreme value distributions and the statistical uncertainty due to limited amount of available data. Furthermore, updating based on new available data is explained based on a Bayesian approach. The statistical uncertainties are estimated based on the Maximum-Likelihood method, and the extreme value estimation uses the peaks-over-threshold (POT) method. Two generic examples of reliability assessments for failure due to fatigue and extreme loads show how the stochastic model can be implemented in reliability assessments.