Vibration-based damage detection in a wind turbine using 1 year of data

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Gustavo Oliveira (Author) Other; Filipe Magalhães (Author) FEUP; Álvaro Cunha (Author) FEUP; Elsa Caetano (Author) FEUP

Journal

Title: Structural Control and Health Monitoring

Vol. 25 No. 11

Pages: 1-22

ISSN: 1545-2255

Publisher: John Wiley & Sons

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 34 Citations

INSPEC

Other information

Authenticus ID: P-00P-M3J

DOI: 10.1002/stc.2238

Abstract (EN): In the current state of development, it is very hard to remotely evaluate the actual condition of wind turbines structures. This makes it impossible for a wind farm owner to correctly deliberate about the need of retrofitting the current wind turbine structure, as well as to decide about the possible extension of its operating lifetime. Taking into account this problem, this paper introduces the main aspects in the development of a vibration-based monitoring system for an onshore 2.0-MW wind turbine based on the identification of the modal properties of the most important vibration modes. Initially, the wind turbine is introduced, as well as the implemented monitoring system. Then, the main steps of the monitoring system are briefly described. A detailed attention is given to the statistical procedure based on regression models used to minimize the influence of operational and environmental effects over the features used to detect structural changes in the wind turbine. Lastly, the suitability of the system to detect damages is thus assessed, through the analysis of three common damage scenarios: onshore foundation damage; damage by scour on an offshore foundation; and blade damage. It is concluded that the system is capable of detecting damage in onshore and offshore foundations, as well as in rotor blades. The most important contribution of the paper is the definition of monitoring framework for damage detection, its implementation, and validation with monitoring data collected during more than 1 year in a utility-scale wind turbine.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22