Operational model updating of low-order horizontal axis wind turbine models for structural health monitoring applications

Abstract

Rotational machinery such as horizontal axis wind turbines exhibits complex and nonlinear dynamics (e.g. precession and Coriolis effects, torsional coupling) and is subjected to nonlinear constrained conditions (i.e. aeroelastic interaction). For those reasons, aeroelastic and computer-aided models reproduced under controlled conditions may fail to predict the correct non-stationary loading and resistance patterns of wind turbines in actual operation. Operational techniques for extracting modal properties under actual non-stationary loadings are needed in order to improve computer-aided elasto-aerodynamic models to better characterize the actual behavior of horizontal axis wind turbines in operational scenarios, monitor and diagnose the system for integrity and damage through time, and optimize control systems. For structural health monitoring applications, model updating of stochastic aerodynamic problems has gained interest over the past decades. A probability theory framework is employed in this study to update a horizontal axis wind turbine model using such a stochastic global optimization approach. Structural identification is addressed under regular wind turbine operation conditions for non-stationary, unmeasured, and uncontrolled excitations by means of stochastic subspace identification techniques. This numerical framework is then coupled with an adaptive simulated annealing numerical engine for solving the problem of model updating. Numerical results are presented for an experimental deployment of a small horizontal axis wind turbine structure.