Time-frequency methods for signal analysis in wind turbines

12th European Workshop on Advanced Control and Diagnosis

Kalista, Karel^1 ; Liska, Jindrich^1

University of West Bohemia in Pilsen, NTIS, Technicka 8, Pilsen

30614, Czech Republic^1

关键词: Condition monitoring systems;    Hilbert Huang transforms;    Nonstationary signals;    Real measured data;    Renewable energies;    Short time Fourier transforms;    Time-frequency methods;    Unsteady conditions;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012012/pdf
DOI  :  10.1088/1742-6596/659/1/012012

来源: IOP

【 摘 要 】

Since wind turbines became one of the most often source of renewable energy, appropriate health and condition monitoring systems are required. Especially proper monitoring of offshore plants is very significant because the accessibility is difficult and inspections are very costly. In comparison with conventional rotating machine vibration monitoring, where steady conditions and stationary signal are usually assumed, the wind turbines are characterized by unsteady conditions due to variable rotational speed. Hence the vibration signal is non-stationary and interpretation of signal signatures may be more complex. The common approach to analyze such non-stationary signals is the use of a time-frequency method, usually Short-Time Fourier Transform, which is the most popular one due to its simplicity. Nevertheless, there are other methods which can give a different view at the analyzed data and provide new information. This article investigates the potential use of some other time-frequency methods, namely Wavelet Transform, Wigner-Ville distribution and Hilbert-Huang transform in wind plants monitoring systems and apply these methods to real measured data with additional simulated bearing fault signal. Finally, the mentioned methods are compared based on computational complexity, readability and interpretability. Though the last two criteria are very subjective, Short-Time Fourier Transform was finally chosen as the most effective method followed by Wavelet Transform.