TY - GEN
T1 - Data-driven Sensor Fault Estimation for the Wind Turbine Systems
AU - Rahimilarki, Reihane
AU - Gao, Zhiwei
AU - Jin, Nanlin
AU - Binns, Richard
AU - Zhang, Aihua
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - As the need for early fault detection increases day by day in large industries, the importance of a reliable fault diagnosis becomes more obvious. Moreover, sensors in industrial systems are prone to faults or malfunctions due to aging or accidents. Motivated by the above, in this study, a neural network sensor fault diagnosis approach is proposed and the stability and convergence of the algorithm are proven by using the robust estimation theorem and input-to-state stability (ISS). The proposed algorithm is applied to a wind turbine benchmark with 4.8 MW rated power. 10% to 30% of the sensor performance reduction is considered to illustrate the effective performance of the addressed algorithm.
AB - As the need for early fault detection increases day by day in large industries, the importance of a reliable fault diagnosis becomes more obvious. Moreover, sensors in industrial systems are prone to faults or malfunctions due to aging or accidents. Motivated by the above, in this study, a neural network sensor fault diagnosis approach is proposed and the stability and convergence of the algorithm are proven by using the robust estimation theorem and input-to-state stability (ISS). The proposed algorithm is applied to a wind turbine benchmark with 4.8 MW rated power. 10% to 30% of the sensor performance reduction is considered to illustrate the effective performance of the addressed algorithm.
KW - Data-driven methods
KW - artificial neural network (ANN)
KW - robust LMI performance
KW - sensor faults
KW - wind turbine
UR - http://www.scopus.com/inward/record.url?scp=85089531015&partnerID=8YFLogxK
U2 - 10.1109/ISIE45063.2020.9152490
DO - 10.1109/ISIE45063.2020.9152490
M3 - Conference Proceeding
AN - SCOPUS:85089531015
T3 - IEEE International Symposium on Industrial Electronics
SP - 1211
EP - 1216
BT - 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 29th IEEE International Symposium on Industrial Electronics, ISIE 2020
Y2 - 17 June 2020 through 19 June 2020
ER -