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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/23234

Title: Advanced algorithms for wind turbine condition monitoring and fault diagnosis
Authors: Ibrahim, Raed Khalaf
Watson, Simon J.
Keywords: Wind turbine
Condition monitoring
Current signature
Fault signature
Fault detection
Issue Date: 2016
Publisher: Wind Europe
Citation: IBRAHIM, R.K. and WATSON, S.J., 2016. Advanced algorithms for wind turbine condition monitoring and fault diagnosis. Presented at the WindEurope Summit 2016, Hamburg, 27-29th Sept.
Abstract: The work undertaken in this research focuses on advanced condition monitoring and fault detection methods for wind turbines (WTs). Fourier Transform (FFT) and Short Time Fourier transform (STFT) algorithms are proposed to effectively extract fault signatures in generator current signals (GCS) for WT fault diagnosis. With this aim, a WT model has been implemented in the MATLAB/Simulink environment to validate the effectiveness of the proposed algorithms. The results obtained with this model are validated with experimental data measured from a physical test rig. The detection of rotor eccentricity is discussed and conclusions drawn on the applicability of frequency tracking algorithms. The newly developed algorithms are compared with a published method to establish their advantages and limitations.
Description: This is a conference paper.
Version: Published
URI: https://dspace.lboro.ac.uk/2134/23234
Publisher Link: https://windeurope.org/summit2016/
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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