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Lookup NU author(s): Dr Pu Shi, Dr Wenxian YangORCiD, Dr Wenye Tian
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Wind turbines today, particularly offshore, are experiencing numerous reliability challenges. How to make the wind industry more profitable by utilizing condition monitoring has become an important research topic in both academic and industry communities. To instantly understand the actual health condition of a wind turbine, much effort has been spent in the past few years to develop various types of online wind turbine condition monitoring techniques. However, these techniques are mainly based on traditional spectral analyses and, therefore, they cannot meet the requirement of interpreting nonlinear, non-stationary wind turbine condition monitoring signals. The empirical mode decomposition (EMD) approach provides a potential solution for this challenge. This research introduces the local and online smoothing sifting process for EMD, as a substitute for the traditional sifting process. In this method, the local mean of the signal at each point is extracted by applying smoothing filters to its adjacent data points, within a variable span sliding window. This approach is direct, local and online. Hence, it can improve the EMD performance and overcome many drawbacks of the classical algorithms. The effectiveness of the proposed approach has been validated by using experimental data which is discussed in the paper. It is these same experiments that show that local EMD can potentially be a powerful tool for conducting online wind turbine condition monitoring.
Author(s): Shi P, Yang Wenxian, McKeever P, Tian W, Ng C, Lee H
Editor(s): European Wind Energy Association
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: European Wind Energy Association & Exhibition Annual Event (EWEA 2015)
Year of Conference: 2015
Online publication date: 20/11/2015
Acceptance date: 14/09/2015
Publisher: European Wind Energy Association
Library holdings: Search Newcastle University Library for this item
ISBN: 9782930670003
