Probabilistic Fatigue Evaluation of Floating Wind Turbine using Combination of Surrogate Model and Copula Model

[1]  M. Rosenblatt Remarks on a Multivariate Transformation , 1952 .

[2]  R. Montes-Iturrizaga,et al.  Environmental contours using copulas , 2015 .

[4]  Erik Vanem,et al.  Joint statistical models for significant wave height and wave period in a changing climate , 2016 .

[5]  Yi Zhang,et al.  Long-term performance assessment and design of offshore structures , 2015 .

[6]  Frank Sehnke,et al.  Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks , 2018, Renewable Energy.

[7]  R. Montes-Iturrizaga,et al.  Development of environmental contours using Nataf distribution model , 2013 .

[8]  J. Jonkman,et al.  Definition of a 5-MW Reference Wind Turbine for Offshore System Development , 2009 .

[9]  Nicolas Gayton,et al.  A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models , 2013, Reliab. Eng. Syst. Saf..

[10]  Alberto Lamberti,et al.  Coastal flooding: A copula based approach for estimating the joint probability of water levels and waves , 2015 .

[11]  A. Kiureghian,et al.  Multivariate distribution models with prescribed marginals and covariances , 1986 .

[12]  John Dalsgaard Sørensen,et al.  Uncertainty propagation through an aeroelastic wind turbine model using polynomial surrogates , 2018 .

[13]  E. Heredia-Zavoni,et al.  Assessment of uncertainty in environmental contours due to parametric uncertainty in models of the dependence structure between metocean variables , 2017 .

[14]  Hamidreza Jafarnejadsani,et al.  Adaptive Control of a Variable-Speed Variable-Pitch Wind Turbine Using Radial-Basis Function Neural Network , 2013, IEEE Transactions on Control Systems Technology.

[15]  Jörg R. Seume,et al.  Investigation of Site-Specific Wind Field Parameters and Their Effect on Loads of Offshore Wind Turbines , 2012 .

[16]  Derek D. Stretch,et al.  Simulating a multivariate sea storm using Archimedean copulas , 2013 .

[17]  N. Jenkins,et al.  Wind Energy Handbook: Burton/Wind Energy Handbook , 2011 .

[18]  I. Sobol On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .

[19]  C. Sallaberry,et al.  Application of principal component analysis (PCA) and improved joint probability distributions to the inverse first-order reliability method (I-FORM) for predicting extreme sea states , 2016 .

[20]  Felice D'Alessandro,et al.  Practical guidelines for multivariate analysis and design in coastal and off-shore engineering , 2014 .

[21]  Carlos Guedes Soares,et al.  Adaptive surrogate model with active refinement combining Kriging and a trust region method , 2017, Reliab. Eng. Syst. Saf..

[22]  M. Sklar Fonctions de repartition a n dimensions et leurs marges , 1959 .

[23]  Claudia Czado,et al.  Selecting and estimating regular vine copulae and application to financial returns , 2012, Comput. Stat. Data Anal..

[24]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[25]  Dag Myrhaug,et al.  Statistical properties of successive wave heights and successive wave periods , 2004 .

[26]  Wei Liu,et al.  Bivariate maximum entropy distribution of significant wave height and peak period , 2013 .

[27]  D. Zafirakis,et al.  The wind energy (r)evolution: A short review of a long history , 2011 .

[28]  Philip Jonathan,et al.  Modeling the Seasonality of Extreme Waves in the Gulf of Mexico , 2011 .

[29]  John Dalsgaard Sørensen,et al.  Assessment of Wind Turbine Structural Integrity using Response Surface Methodology , 2016 .

[30]  Elzbieta M. Bitner-Gregersen,et al.  Joint met-ocean description for design and operations of marine structures , 2015 .

[31]  Sancho Salcedo-Sanz,et al.  Short-term wind speed prediction in wind farms based on banks of support vector machines , 2011 .