Open AccessFault diagnosis of wind turbine based on multi‐sensors information fusion technology
Author(s) -
Hang Jun,
Zhang Jianzhong,
Cheng Ming
Publication year - 2014
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.2013.0123
Subject(s) - fault (geology) , hilbert–huang transform , turbine , information fusion , computer science , pattern recognition (psychology) , fuzzy logic , sensor fusion , fusion , feature (linguistics) , artificial intelligence , data mining , vibration , engineering , wind power , control engineering , computer vision , mechanical engineering , linguistics , philosophy , physics , electrical engineering , filter (signal processing) , quantum mechanics , seismology , geology
Faults occurring in wind turbine (WT) degrade the performance and efficiency of wind power generation system, which result in large operation and maintenance cost of the WT. Therefore effective fault diagnosis schemes are greatly required for WT. This study presents a multi‐sensors information fusion technology for fault diagnosis of the WT, where vibration sensors are adopted. Firstly, to deal with the non‐stationary nature of the vibration signals of the WT, empirical mode decomposition method is utilised to extract fault features from the signals. Secondly, different classifiers are trained separately to classify fault types based on independent feature vectors from different sensors. Finally, three fusion approaches, named ordered weighted averaging, D‐S evidential reasoning and fuzzy integral, are, respectively, used for fusing the diagnosis results of individual classifiers. To validate the proposed method, a direct‐drive WT test rig is constructed and the related experiments are carried out. The experimental results validate the assumption that the proposed approach is effective for fault diagnosis of the WT, which has a higher diagnostic accuracy than that of individual sensor.