TY - GEN
T1 - Hybrid Approach integrated with Gaussian Process Regression for Condition Monitoring Strategies at the Rotor side of a Doubly-fed Induction Generator
AU - Zhang, Shuo
AU - Robinson, Emma
AU - Basu, Malabika
N1 - Publisher Copyright:
© 2022 ESREL2022 Organizers. Published by Research Publishing, Singapore.
PY - 2022
Y1 - 2022
N2 - Regression-based Machine Learning (ML) approaches are mainly applied to fit the power curve for the performance evaluation of wind turbines (WTs). Although a fitted power curve is prevalent and straightforward for anomaly detection, it is difficult to identify the fault types at the rotor side of a WT, particularly, because the operation can be dependent on multiple parameters. The present paper suggests an interesting approach towards condition monitoring (CM) and fault diagnosis of a DFIG by only processing rotor currents through several signal processing techniques to recognize and localize miscellaneous electrical disturbances. A non-parametric regression approach, Gaussian process regression (GPR), is advised to fit the no-fault performance curve (PC) of rotor current standard deviation (SD) versus wind speed. Thereafter, a hybrid approach with GPR is investigated to visualize no-fault operation, yield the anomaly, and conduct fault recognition at the rotor side with outstanding validation scores in terms of accuracy, dependability, and security.
AB - Regression-based Machine Learning (ML) approaches are mainly applied to fit the power curve for the performance evaluation of wind turbines (WTs). Although a fitted power curve is prevalent and straightforward for anomaly detection, it is difficult to identify the fault types at the rotor side of a WT, particularly, because the operation can be dependent on multiple parameters. The present paper suggests an interesting approach towards condition monitoring (CM) and fault diagnosis of a DFIG by only processing rotor currents through several signal processing techniques to recognize and localize miscellaneous electrical disturbances. A non-parametric regression approach, Gaussian process regression (GPR), is advised to fit the no-fault performance curve (PC) of rotor current standard deviation (SD) versus wind speed. Thereafter, a hybrid approach with GPR is investigated to visualize no-fault operation, yield the anomaly, and conduct fault recognition at the rotor side with outstanding validation scores in terms of accuracy, dependability, and security.
KW - Condition monitoring (CM)
KW - Gaussian process regression (GPR)
KW - Machine Learning (ML)
KW - Performance curve (PC)
KW - Standard deviation (SD)
KW - Wind turbines (WTs)
UR - http://www.scopus.com/inward/record.url?scp=85208258499&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5183-4_S30-07-611-cd
DO - 10.3850/978-981-18-5183-4_S30-07-611-cd
M3 - Conference contribution
AN - SCOPUS:85208258499
SN - 9789811851834
T3 - Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future
SP - 3127
EP - 3134
BT - Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future
A2 - Leva, Maria Chiara
A2 - Patelli, Edoardo
A2 - Podofillini, Luca
A2 - Wilson, Simon
PB - Research Publishing Services
T2 - 32nd European Safety and Reliability Conference, ESREL 2022
Y2 - 28 August 2022 through 1 September 2022
ER -