Vision-based Dynamics Monitoring (VDM) for Diagnosing the Variations of Wind Turbine Tower Foundation Conditions

Abstract

A slight uneven settlement of the foundation may cause the wind turbine to shake, tilt or even collapse, so it is increasingly necessary to realize remote condition monitoring of the foundations. At present, the wind turbine foundation monitoring system is incomplete. The current monitoring research of the tower foundation is mainly of contact measurements, using acceleration sensors and static level sensors for monitoring multiple reference points. Such monitoring methods will face some disadvantages, such as the complexity of monitoring deployment, the cost of manpower, and the load effect on the tower structure. To solve above issues, this paper aims to investigate wind turbine tower foundation variation dynamic monitoring based on machine vision. Machine vision monitoring is a kind of non-contact measurement, which help to realize comprehensive diagnosis of early foundation uneven settlement and loose faults. The FEA model is firstly investigated as the theoretical foundation to investigate the dynamics of the tower foundation. Secondly, the Gaussian-based vibration detection is adopted by tracking the tower edge points. Finally, a tower structure with distributed foundation support is tested. The modal parameters obtained from the visual measurement are compared with those from the accelerometer, proving the vision method can effectively monitor the issues with tower foundation changes.  

Conflict of Interest Statement

The authors declare no conflicts of interest.