Abstract
With appropriate vibration modeling and analysis the incipient failure of key components such as the tower, drive train and rotor of a large wind turbine can be detected. In this paper, the Nonlinear State Estimation Technique (NSET) has been applied to model turbine tower vibration to good effect, providing an understanding of the tower vibration dynamic characteristics and the main factors influencing these. The developed tower vibration model comprises two different parts: a sub-model used for below rated wind speed; and another for above rated wind speed. Supervisory control and data acquisition system (SCADA) data from a single wind turbine collected from March to April 2006 is used in the modeling. Model validation has been subsequently undertaken and is presented. This research has demonstrated the effectiveness of the NSET approach to tower vibration; in particular its conceptual simplicity, clear physical interpretation and high accuracy. The developed and validated tower vibration model was then used to successfully detect blade angle asymmetry that is a common fault that should be remedied promptly to improve turbine performance and limit fatigue damage. The work also shows that condition monitoring is improved significantly if the information from the vibration signals is complemented by analysis of other relevant SCADA data such as power performance, wind speed, and rotor loads.
Highlights
Vibration can be a good indicator of the operating conditions of a range of mechanical components and structures and can support condition monitoring of important wind turbine components such as the rotor, drive train and tower [1,2]
The key steps for vibration modeling with Nonlinear State Estimation Technique (NSET) are in sequence: selection of the relevant variables to make up the observation vector and construct the memory matrix using the SCADA data obtained from the wind turbine during periods of normal operation
After point 275, the tower vibration was much higher than before and the difference between the two was sharply increased. This abnormal change in the relationship between these variables is detected in a timely manner by the tower vibration model (TVM) and the residuals change in a statistically way after this point
Summary
Vibration can be a good indicator of the operating conditions of a range of mechanical components and structures and can support condition monitoring of important wind turbine components such as the rotor, drive train and tower [1,2]. When the wind speed is above the rated one, the blade angle will normally be adjusted to maintain the rated power This will result in changes to the aerodynamic forces acting on the rotor, and can lead directly to changes in tower vibration (both frequencies and amplitudes). The work reported in [5] starts from basic laws of physics applied to the gearbox to derive robust relationships between temperature, efficiency, rotational speed and power output With this relationship, an abnormal rise in the gearbox oil temperature as represented in the SCADA data can be used to predict gearbox failure.
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