Browse by author
Lookup NU author(s): Dr Wenxian YangORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2015.
For re-use rights please refer to the publisher's terms and conditions.
The time-varying operational conditions applied to wind turbines (WTs) not only challenge their operation but also make condition monitoring (CM) difficult. To achieve a reliable CM result, more advanced signal processing techniques, rather than the conventional spectral analyses, are urgently needed for interpreting the non-linear and non-stationary (NNS) CM signals collected from the turbines. The work presented in this paper is an effort for meeting such a requirement. Based on the proven capability of the Spline-kernelled Chirplet transform (SCT) in detecting the instantaneous frequencies (IF) within NNS mono-component signals, the paper improves the SCT to enable it to detect the instantaneous amplitude (IA) of lengthy NNS multi-component signals at a fault-related frequency of interest. The improved SCT is then applied for developing a new real-time CM technique dedicated to extracting fault-related features from WT CM signals. Experiment proves that the improved SCT has overcome existing SCT issues and is capable of correctly tracking the amplitude characteristics of NNS multi-component signals at fault-related frequencies of interest. The new CM technique developed, based on this improved SCT, shows success in detecting both mechanical and electrical faults occurring in a WT drive train, despite the constantly varying operational conditions of the turbine. Moreover, its algorithm is efficient in computation, which not only enables it to deal with lengthy NNS CM signals but makes it ideal for online use.
Author(s): Yang W, Tavner P, Tian W
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Industrial Electronics
Print publication date: 01/10/2015
Online publication date: 20/07/2015
Acceptance date: 02/07/2015
Date deposited: 23/08/2016
ISSN (print): 0278-0046
ISSN (electronic): 1557-9948
Altmetrics provided by Altmetric