TY - JOUR
T1 - Data-driven technique for interpreting wind turbine condition monitoring signals
AU - Yang, Wenxian
AU - Little, Christian
AU - Tavner, Peter J.
AU - Court, Richard
PY - 2014/3/1
Y1 - 2014/3/1
N2 - 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 non-linear and non-stationary in nature because of the constantly varying loads and non-linear operations of the turbines. To deal with this issue, a new data-driven signal processing technique is developed in this study 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 with the Hilbert-Huang transform 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.
AB - 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 non-linear and non-stationary in nature because of the constantly varying loads and non-linear operations of the turbines. To deal with this issue, a new data-driven signal processing technique is developed in this study 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 with the Hilbert-Huang transform 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.
KW - condition monitoring
KW - Hilbert transforms
KW - power generation economics
KW - signal processing
KW - wind turbines
KW - wind turbine condition monitoring signals
KW - WT offshore
KW - reliable condition monitoring techniques
KW - reliable CM techniques
KW - economic return
KW - WT CM signals
KW - constantly varying loads
KW - signal testing
KW - Hilbert-Huang transform
KW - EOSA
KW - energy operator separation algorithym
KW - ITD
KW - instrinsic time-scale decomposition
KW - data-driven signal processing technique
KW - nonlinear turbine operations
UR - http://www.scopus.com/inward/record.url?scp=84896841338&partnerID=8YFLogxK
U2 - 10.1049/iet-rpg.2013.0058
DO - 10.1049/iet-rpg.2013.0058
M3 - Article
AN - SCOPUS:84896841338
VL - 8
SP - 151
EP - 159
JO - IET Renewable Power Generation
JF - IET Renewable Power Generation
SN - 1752-1416
IS - 2
ER -