@inproceedings{fbc0165ee761458ba7f464a426a4e299,
title = "Fault detection of a benchmark wind turbine using interval analysis",
abstract = "This paper investigates a state estimation set- membership approach for fault detection of a benchmark wind turbine. The main challenges in the benchmark are high noise on the wind speed measurement and the nonlinearities in the aerodynamic torque such that the overall model of the turbine is nonlinear. We use an effective wind speed estimator to estimate the effective wind speed and then using interval analysis and monotonicity of the aerodynamic torque with respect to the effective wind speed, we can apply the method to the nonlinear system. The fault detection algorithm checks the consistency of the measurement with a closed set that is computed based on the past measurements and a model of the system. If the measurement is not consistent with this set, a fault is detected. The result demonstrates effectiveness of the method for fault detection of the benchmark wind turbine.",
keywords = "Fault Detection, wind turbine",
author = "Tabatabaeipour, {Seyed Mojtaba} and Odgaard, {Peter Fogh} and Thomas Bak",
year = "2012",
month = jun,
day = "27",
language = "English",
isbn = "978-1-4577-1095-7",
series = "American Control Conference",
publisher = "American Automatic Control Council",
pages = "4387--4392",
booktitle = "2012 American Control Conference (ACC)",
note = "2012 American Control Conference ; Conference date: 27-06-2012 Through 29-06-2012",
}