Probabilistic extreme response analysis of large wind turbines to natural winds

Date

2014-08

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

With increases in size and flexibility of modern wind turbines, especially for offshore applications, an improved understanding and assessment of turbine performance under various wind conditions is increasingly important to greatly reduce the risk and increase turbine efficiency and reliability. The objective of this research is to establish advanced modeling and simulation frameworks for better quantifying long-term probabilistic extreme responses of utility-scale wind turbines in order to achieve reliability- and performance-based turbine design.

An eigenvalue analysis of the aeroelastic turbine system is conducted under different wind conditions using a linear state-space model linearized about the steady-state equilibrium. The eigenvalues and eigenvectors are used to determine the potential flutter instabilities and inter-modal coupling of wind turbine system under both operational and parked conditions. A comprehensive dynamic response analysis is performed to identify the role of wind loads on blades and tower played on the generation of wind turbine dynamic response. This study investigates the combination rules for predicting extreme resultant tower response, which is combined from response components product by wind loads on blades and tower. In current wind turbine design practice, the tower extreme response due to wind loads on blades is often calculated using an equivalent static load modeling in terms of concentrated force and moment at the tower top. An improved equivalent static load model is presented with additional distributed inertial force on tower, which results in better prediction of tower extreme response.

In current wind turbine design standard, the long-term extreme responses of operational and parked wind turbines with various mean recurrence intervals are estimated from probability distribution of short-term 10-min extreme response under the assumption that the short-term extremes are statistically independent. The adequacy of this critical assumption is examined through a Monte Carlo simulation procedure. For a parked wind turbine, the long-term extreme response is often dominated by extreme response under strong winds. The upper tail of wind speed distribution may not be well represented by the distribution model determined from bulk wind speed data. Improved methods are proposed for estimating long-term extreme response of parked turbine by using more accurate modeling of distribution tail of 10-min mean wind speed.

A comprehensive study is also conducted concerning the influence of non-Gaussian wind characteristics on wind turbine response under both operational and parked conditions. The non-Gaussian wind field is simulated with specified non-Gaussian statistics and power spectral characteristics. The wind turbine response time histories at various mean wind speeds are generated. The turbine response statistical moments and long-term extreme responses influenced by the non-Gaussian wind inflow characteristics are investigated by comparing to those under Gaussian wind inflow. New insights on the determination of extreme response distribution from the random process method are also presented focusing on a better modeling of the response distribution tail.

Assessment of structural performance under stochastic dynamic loadings requires estimation of the extremes of stochastic response components and the resultant responses as their linear and nonlinear combinations. A framework for evaluating scalar and vectorial combined resultant responses from two response components is presented by advanced upcrossing rate theory. An extensive parameter study is conducted concerning the influence of statistical moments of non-Gaussian response components on the extremes of resultant responses. This study also proposes improved formulas for estimating extremes of resultant responses directly from the extremes of response components.

Description

Keywords

Extreme value, Wind turbine, Extreme wind speed, Instability, Structural dynamics

Citation