Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.

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Grand challenges in the science of wind energy

Design Of Steel Structures

The paper presents a detailed review of the state-of-the-art research activities on structural reliability analysis of wind turbines between the 1990s and 2017. We describe the reliability methods including the first- and second-order reliability methods and the simulation reliability methods and show the procedure for and application areas of structural reliability analysis of wind turbines. Further, we critically review the various structural reliability studies on rotor blades, bottom-fixed support structures, floating systems and mechanical and electrical components. Finally, future applications of structural reliability methods to wind turbine designs are discussed.

https://www.mdpi.com/1996-1073/10/12/2099/pdf

Structural Reliability Analysis of Wind Turbines: A Review

https://hal.archives-ouvertes.fr/hal-02294637/document

Surrogate model uncertainty in wind turbine reliability assessment

of the simple linear regression model. Multiple linear regression for two variables is discussed in Chapter 8, and that for more than two variables is covered in Chapter 9. Chapter 10, on model building, is perhaps the book’s strongest chapter. The authors provide one of the most intuitive discussions on variable transformations that I have seen. Nice presentations of indicator variables, variable selection, and influence diagnostics are also provided. The final chapter covers a wide variety of topics, including analysis of variance models, logistic regression, and robust regression. The coverage of regression is not matrix-based, but optional linear algebra sections at the end of each chapter are useful for one wishing to use matrices. In general, the writing is clear and conceptual. A good number of exercises (about 20 on average) at the end of each chapter are provided. The exercises emphasize derivations and computations. It is difficult to name some comparison texts. Certainly, the text by Ott and Longnecker (2001) would be more suitable for a statistical methods course for an interdisciplinary audience. The regression texts of Montgomery, Peck, and Vining (2001) and Mendenhall and Sincich (2003) are more comprehensive in the regression treatment than the reviewed text. However, Introduction to Linear Models and Statistical Inference is not meant to compete with these texts—rather, its audience is primarily those taking a statistics course within a mathematics department.

Quality by Experimental Design

Ultimate loads and response analysis of a monopile supported offshore wind turbine using fully coupled simulation

This PhD research is funded by Lloyd’s Register Group Services Ltd., Aberdeen. Sriramula’s work within the Lloyd’s Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

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N Krishnan

Papers

Ultimate loads and response analysis of a monopile supported offshore wind turbine using fully coupled simulation

Kriging models for aero-elastic simulations and reliability analysis of offshore wind turbine support structures

Reliability analysis of offshore wind turbine support structures using Kriging models

A computational framework for the reliability of offshore wind turbine support structures