Condition monitoring and vibration analysis of wind turbine

Feng Xiao,Isaac Wait,Benjamin Still,Gang S Chen,Chen Tian,Zhaohui (Joey) Yang

doi:10.1177/1687814020913782

Abstract

Health condition monitoring through comprehensive monitoring, incipient fault diagnosis, and the prediction of impending faults allows for the promotion of the long-term performance of wind turbines, particularly those in harsh environments such as cold regions. The condition monitoring of wind turbines is characterized by the difficulties associated with the lack of measured data and the nonstationary, stochastic, and complicated nature of vibration responses. This article presents a characterization of the vibrations of an operational wind turbine by spectrogram, scalogram, and bi-spectrum analyses. The results reveal varied nonstationary stochastic properties and mode-coupling instability in the vibrations of the tested wind turbine tower. The analysis illustrates that the wind turbine system vibrations exhibit certain non-Gaussian stochastic properties. An analytical model is used to evaluate the nonstationary, stochastic phenomena and mode-coupling phenomena observed in the experimental results. These results are of significance for the fault diagnosis of wind turbine system in operation as well as for improving fatigue designs beyond the wind turbulence spectral models recommended in the standards.

Highlights

Wind energy has become an important type of sustainable energy, and with the increased demand for wind energy, its maintenance has become very important
Wind turbine system fatigue designs have been based on the assumption of Gaussian processes of wind turbulence load, which may be substantially different from the real wind load
This study presents the dynamic response recorded by a wind turbine monitoring system in Alaska for varied seasons

Summary

Introduction

Wind energy has become an important type of sustainable energy, and with the increased demand for wind energy, its maintenance has become very important. To perform condition monitoring using output data, it is necessary to characterize the nonstationary stochastic vibrations of the wind turbine. The stochastic features are characterized using the bi-spectrum, which indicates that the wind turbine vibrations are non-Gaussian processes with nonstationary stochastic features. The higher order spectrum analysis is a useful method to characterize complex stochastic processes and distinguish varied processes from various conditions. Wind turbine vibration signals often have nonstationary stochastic features in addition to complex mode coupled vibrations and are often corrupted by the noise from the wind turbulence. Figure 10. 3D bi-spectrum of the signal in September 2014 and its contour

Discussion of results

Conclusion