Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Shahriar, Md Rifat, Wang, Longyan, Kan, Man Shan, Tan, Andy, & Ledwich, Gerard (2015) Fault detection of wind turbine drivetrain utilizing power-speed characteristics. In 9th WCEAM Research Papers: Volume 1, Proceedings of 2014 World Congress on Engineering Asset Management [Lecture Notes in Mechanical Engineering], Springer International Publishing, pp. 143-155.

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Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.

Impact and interest:

2 citations in Scopus
2 citations in Web of Science®
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ID Code: 84138
Item Type: Conference Paper
Refereed: Yes
Keywords: Wind turbine assets, Condition monitoring, Fault detection
DOI: 10.1007/978-3-319-15536-4_12
ISSN: 2195-4356
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: © Copyright 2015 by Springer International Publishing Switzerland
Deposited On: 14 May 2015 01:11
Last Modified: 24 Jun 2017 14:44

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