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Data-driven technique for interpreting wind turbine condition monitoring signals

Lookup NU author(s): Dr Wenxian YangORCiD, Christian Little


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Increasing deployment of large wind turbines (WT) offshore and in remote areas requires reliable condition monitoring (CM) techniques to guarantee the high availability of these WTs and economic return. To meet this need, much effort has been expended to improve the capability of analysing the WT CM signals. However, a fully satisfactory technique has not been achieved today. One of the major reasons is that the developed techniques still cannot provide accurate interpretation of the WT CM signals, which are usually nonlinear and non-stationary in nature due to the constantly varying loads and nonlinear operations of the turbines. To deal with this issue, a new data-driven signal processing technique is developed in this paper based on the concepts of Intrinsic Time-scale Decomposition (ITD) and Energy Operator Separation Algorithm (EOSA). The advantages of the proposed technique over the traditional data-driven techniques have been demonstrated and validated experimentally. It has been shown that in comparison to the Hilbert-Huang Transform (HHT) the combination of ITD and EOSA provided more accurate and explicit presentations of the instantaneous information of the signals tested. Thus, it provides a much improved offline tool for accurately interpreting WT CM signals.

Publication metadata

Author(s): Yang W, Little C, Tavner PJ, Court R

Publication type: Article

Publication status: Published

Journal: IET Renewable Power Generation

Year: 2014

Volume: 8

Issue: 2

Pages: 151-159

Print publication date: 03/10/2013

ISSN (print): 1752-1416

ISSN (electronic): 1752-1424

Publisher: The Institution of Engineering and Technology


DOI: 10.1049/iet-rpg.2013.0058


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