TY - GEN
T1 - Multiple actuator fault classification in wind turbine systems using multi-linear principal component analysis techniques
AU - Fu, Yichuan
AU - Liu, Yuanhong
AU - Gao, Zhiwei
N1 - Funding Information:
ACKNOWLEDGEMENTS The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under grant 61673074.
Funding Information:
The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under grant 61673074.
Publisher Copyright:
© 2019 Chinese Automation and Computing Society in the UK-CACSUK.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/9
Y1 - 2019/9
N2 - Monitoring and fault diagnosis plays a key role in improving the reliability, availability and productiveness of wind turbine systems. When a wind turbine is subjected to multiple faults, it is even more challenging to identify and classify the faults. In this paper, multi-linear principal component analysis (MPCA) is employed to extract the significant features of a wind turbine for the purpose of fault classification of multiple faults. Simulations and validations are performed in terms of faulty data sets generated by a 4.8-MW wind turbine benchmark system subjected to two actuator faults.
AB - Monitoring and fault diagnosis plays a key role in improving the reliability, availability and productiveness of wind turbine systems. When a wind turbine is subjected to multiple faults, it is even more challenging to identify and classify the faults. In this paper, multi-linear principal component analysis (MPCA) is employed to extract the significant features of a wind turbine for the purpose of fault classification of multiple faults. Simulations and validations are performed in terms of faulty data sets generated by a 4.8-MW wind turbine benchmark system subjected to two actuator faults.
KW - Actuator faults
KW - Fault classification
KW - Multi-linear principal component analysis (MPCA)
KW - Wind turbines
UR - https://www.scopus.com/pages/publications/85075799766
U2 - 10.23919/IConAC.2019.8895143
DO - 10.23919/IConAC.2019.8895143
M3 - Conference contribution
AN - SCOPUS:85075799766
T3 - ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
BT - ICAC 2019 - 2019 25th IEEE International Conference on Automation and Computing
A2 - Yu, Hui
PB - IEEE
CY - Piscataway, NJ
T2 - 25th IEEE International Conference on Automation and Computing, ICAC 2019
Y2 - 5 September 2019 through 7 September 2019
ER -
