Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring

Abstract

Due to constantly varying wind speed, wind turbine (WT) components often operate at variable speeds in order to capture more energy from wind. As a consequence, WT condition monitoring (CM) signals always contain intra-wave features, which are difficult to extract through performing conventional time–frequency analysis (TFA) because none of which is locally adaptive. So far, only empirical mode decomposition (EMD) and its extension forms can extract intra-wave features. However, 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 study 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.