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Superiorities of Variational Mode Decomposition over Empirical Mode Decomposition Particularly in Time-frequency Feature Extraction and Wind Turbine Condition Monitoring

Lookup NU author(s): Dr Wenxian YangORCiD, Dr Pu Shi, Dr Wenye Tian

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IET, 2017.

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Abstract

Due to constantly varying wind speed, wind turbine (WT) rotor and the other drive train components often operate at variable speeds in order to capture energy from wind as efficiently as possible and therefore generate more electric power. Due to the variable loads and rotational speed, the condition monitoring (CM) signals collected from WTs always contain intra-wave features, which are difficult to extract through performing conventional Time-Frequency Analysis (TFA) because the successful extraction of these intra-wave characteristics requests a locally adaptive signal processing technique. To now, only Empirical Mode Decomposition (EMD) and its extension form can meet such a requirement. However, practice has shown that the EMD and those EMD-based techniques also suffer a number of defects in TFA (e.g. weak robustness of against noise, unidentified ripples, inefficiency in detecting side-band frequencies, etc.). The existence of these issues has significantly limited the extensive application of the EMD family techniques to WT CM. Recently, an alternative TFA method, namely Variational Mode Decomposition (VMD), was proposed to overcome all these issues. The purpose of this paper is to verify the superiorities of the VMD over the EMD and investigate its potential application to the future WT CM. Experiment has shown that the VMD outperforms the EMD not only in noise robustness but also in multi-component signal decomposition, side-band detection, and intra-wave feature extraction. Thus, it has potential as a promising technique for WT CM.

Publication metadata

Author(s): Yang W, Peng Z, Wei K, Shi P, Tian W

Publication type: Article

Publication status: Published

Journal: IET Renewable Power Generation

Year: 2017

Volume: 11

Issue: 4

Pages: 443-452

Print publication date: 04/05/2017

Online publication date: 27/06/2016

Acceptance date: 19/05/2016

Date deposited: 23/08/2016

ISSN (print): 1752-1416

ISSN (electronic): 1752-1424

Publisher: IET

URL: http://dx.doi.org/10.1049/iet-rpg.2016.0088

DOI: 10.1049/iet-rpg.2016.0088

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