Open AccessPrecise feature extraction from wind turbine condition monitoring signals by using optimised variational mode decomposition
Author(s) -
Shi Pu,
Yang Wenxian
Publication year - 2017
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2016.0716
Subject(s) - hilbert–huang transform , hilbert transform , nonlinear system , turbine , feature extraction , fault (geology) , computer science , mode (computer interface) , fault detection and isolation , decomposition , filter (signal processing) , wind power , condition monitoring , pattern recognition (psychology) , control theory (sociology) , engineering , artificial intelligence , geology , physics , mechanical engineering , electrical engineering , computer vision , operating system , ecology , control (management) , quantum mechanics , seismology , actuator , biology
Reliable condition monitoring (CM) highly relies on the correct extraction of fault‐related features from CM signals. This equally applies to the CM of wind turbines (WTs). Although influenced by slowly rotating speeds and constantly varying loading, extracting fault characteristics from lengthy, nonlinear, non‐stationary WT CM signals is extremely difficult, which makes WT CM one of the most challenge tasks in wind power asset management despites that lots of efforts have been spent. Attributed to the superiorities to empirical mode decomposition and its extension form Hilbert–Huang transform in dealing with nonlinear, non‐stationary CM signals, the recently developed variational mode decomposition (VMD) casts a glimmer of light for the solution for this issue. However, the original proposed VMD adopts default values for both number of modes and filter frequency bandwidth. It is not adaptive to the signal being inspected. As a consequence, it would lead to inaccurate feature extraction thus unreliable WT CM result sometimes. For this reason, a precise feature extraction method based on optimised VMD is investigated. The experiments have shown that thanks to the use of the proposed optimisation strategies, the fault‐related features buried in WT CM signals have been extracted out successfully.