TY - GEN
T1 - Fault classification in wind turbines using principal component analysis technique
AU - Fu, Yichuan
AU - Liu, Yuanhong
AU - Gao, Zhiwei
N1 - Funding Information:
ACKNOWLEDGEMENTS The authors would like to thank the research support from the E&E faculty at University of Northumbria (UK), and the National Nature Science Foundation of China (NNSFC) under grant 61673074.
PY - 2019/7
Y1 - 2019/7
N2 - In this study, dimensionality reduction and feature extraction techniques are applied to fault classification for wind turbine systems using principal component analysis (PCA). Experimentations are performed in terms of two topologies of faulty datasets which are respectively actuator effectiveness loss and sinusoidal fault, occurring in a 4.8-MW wind turbine benchmark system. In order to evaluate the PCA technique, additive white Gaussian noise (AWGN) signals are introduced to the aforementioned two faulty conditions respectively. The experimental results demonstrate that PCA not only can extract the significant features but also distinguish different types of fault effectively in this wind turbine benchmark system.
AB - In this study, dimensionality reduction and feature extraction techniques are applied to fault classification for wind turbine systems using principal component analysis (PCA). Experimentations are performed in terms of two topologies of faulty datasets which are respectively actuator effectiveness loss and sinusoidal fault, occurring in a 4.8-MW wind turbine benchmark system. In order to evaluate the PCA technique, additive white Gaussian noise (AWGN) signals are introduced to the aforementioned two faulty conditions respectively. The experimental results demonstrate that PCA not only can extract the significant features but also distinguish different types of fault effectively in this wind turbine benchmark system.
KW - Additive white Gaussian noise (AWGN)
KW - Dimensionality reduction
KW - Fault classification
KW - Feature extraction
KW - Principal component analysis (PCA)
KW - Wind turbines
UR - http://www.scopus.com/inward/record.url?scp=85075784031&partnerID=8YFLogxK
U2 - 10.1109/INDIN41052.2019.8972303
DO - 10.1109/INDIN41052.2019.8972303
M3 - Conference contribution
AN - SCOPUS:85075784031
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 1303
EP - 1308
BT - Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
PB - IEEE
CY - Piscataway, NJ
T2 - 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Y2 - 22 July 2019 through 25 July 2019
ER -
