Open AccessBlade root moment sensor failure detection based on multibeam LIDAR for fault‐tolerant individual pitch control of wind turbines
Author(s) -
Stotsky Alexander
Publication year - 2014
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.37
Subject(s) - moment (physics) , harmonics , lidar , anemometer , wind power , harmonic , turbine blade , blade pitch , fault detection and isolation , turbine , acoustics , fault (geology) , signal (programming language) , control theory (sociology) , wind speed , structural engineering , computer science , engineering , actuator , geology , remote sensing , voltage , physics , aerospace engineering , artificial intelligence , electrical engineering , oceanography , control (management) , classical mechanics , seismology , programming language
Detection of blade root moment sensor failures is an important problem for fault‐tolerant individual pitch control, which plays a key role in reduction of uneven blade loads of large wind turbines. A new method for detection of blade root moment sensor failures which is based on variations induced by a vertical wind shear is described in this paper. The detection is associated with monitoring of statistical properties of the difference between amplitudes of the first harmonic of the blade load, which is calculated in two different ways. The first method is based on processing of the load sensor signal, which contains a number of harmonics. The first harmonic is recovered via least squares estimation of the blade load signal with harmonic regressor and strictly diagonally dominant ( SDD ) information matrix. The second method is a model‐based method of estimation of the first harmonic, which relies on the blade load model and upwind speed measurements provided by multibeam Light Detection and Ranging (LIDAR). This is a new application for future LIDAR ‐enabled wind turbine technologies. Moreover, adaptation of the load model in a uniform wind field is proposed. This adaptation improves accuracy of the load estimation and hence the performance of the blade load sensor failure detection method.