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Simplified Automatic Fault Detection in Wind Turbine Induction Generators

Brigham, K.; Zappalá, D.; Crabtree, C.J.; Donaghy-Spargo, C.

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K. Brigham

D. Zappalá


This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.


Brigham, K., Zappalá, D., Crabtree, C., & Donaghy-Spargo, C. (2020). Simplified Automatic Fault Detection in Wind Turbine Induction Generators. Wind Energy, 23(4), 1135-1144.

Journal Article Type Article
Acceptance Date Dec 24, 2019
Online Publication Date Jan 20, 2020
Publication Date Apr 30, 2020
Deposit Date Jan 6, 2020
Publicly Available Date Jan 23, 2020
Journal Wind Energy
Print ISSN 1095-4244
Electronic ISSN 1099-1824
Publisher Wiley Open Access
Peer Reviewed Peer Reviewed
Volume 23
Issue 4
Pages 1135-1144


Published Journal Article (Advance online version) (3.6 Mb)

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Copyright Statement
Advance online version © 2020 The Authors. Wind Energy published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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