Condition monitoring of wind turbine drivetrains
state-of-the-art technologies, recent trends, and future outlook
Kayacan Kestel (Vrije Universiteit Brussel)
Xavier Chesterman (Vrije Universiteit Brussel, FlandersMake@VUB - BPandM)
Donatella Zappalá (TU Delft - Aerospace Engineering)
Simon Watson (TU Delft - Aerospace Engineering)
Edward Hart (University of Strathclyde)
James Carroll (University of Strathclyde)
Yolanda Vidal (Universitat Politécnica de Catalunya)
Amir R. Nejad (Norwegian University of Science and Technology (NTNU))
Shawn Sheng (National Laboratory of the Rockies)
More Authors (External organisation)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
As global wind capacity expands, reducing operations and maintenance costs is critical to lowering the levelized cost of energy. This paper explores the state of the art in condition monitoring and prognostic strategies for wind turbine drivetrains, which are among the most failure-prone and maintenance-intensive subsystems. Current diagnostic methodologies are evaluated, covering supervisory control and data acquisition (SCADA) data, high-frequency vibration and acoustic analysis, machine learning and digital twin frameworks. Finally, practical challenges are identified that limit wide-scale industrial adoption, in order to guide future research and industrial efforts.
help_outline