Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis

Chinese Journal of Electrical Engineering ›› 2018, Vol. 4 ›› Issue (3): 80-89.

Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis

Wei Qiao^*, Liyan Qu

Power and Energy Systems Laboratory, Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588-0511, USA

Online:2018-09-25 Published:2019-10-31
Contact: * , E-mail: wqiao3@unl.edu.
About author:Wei Qiao (S’05 - M’08 - SM’12) received a B.Eng. and an M.Eng. degrees in electrical engineering from Zhejiang University, Hangzhou, China, in 1997 and 2002, respectively, an M.S. degree in high-performance computation for engineered systems from Singapore-MIT Alliance, Singapore, in 2003, and a Ph.D. degree in electrical engineering from Georgia Institute of Technology, Atlanta, GA, USA, in 2008. Since August 2008, he has been with the University of Nebraska- Lincoln, Lincoln, NE, USA, where he is currently a Professor with the Department of Electrical and Computer Engineering. His research interests include renewable energy, smart grids, condition monitoring, power electronics, electric machines and drives, and new electrical energy conversion devices. He is the author or coauthor of more than 220 papers in refereed journals and conference proceedings. Dr. Qiao is an Editor of the IEEE Transactions on Energy Conversion and the IEEE Power Engineering Letters, and an Associate Editor of the IEEE Transactions on Power Electronics and the IEEE Journal of Emerging and Selected Topics in Power Electronics. He was the recipient of a 2010 U.S. National Science Foundation CAREER Award and the 2010 IEEE Industry Applications Society Andrew W. Smith Outstanding Young Member Award. Liyan Qu (S’05-M’08-SM’17) received a B.Eng. (with the highest distinction) and an M.Eng. degrees in electrical engineering from Zhejiang University, Hangzhou, China, in 1999 and 2002, respectively, and a Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign, Champaign, IL, USA, in 2007. From 2007 to 2009, she was an Application Engineer with Ansoft Corporation, Irvine, CA, USA. Since January 2010, she has been with the University of Nebraska-Lincoln, Lincoln, NE, USA, where she is currently an Associate Professor with the Department of Electrical and Computer Engineering. Her research interests include energy efficiency, renewable energy, numerical analysis and computer aided design of electric machinery and power electronic devices, dynamics and control of electric machinery, and magnetic devices. Dr. Qu was a recipient of the 2016 U.S. National Science Foundation CAREER Award.
Supported by:
This work was supported in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy under Awards Number DE-EE0006802 and DE-EE0001366, and in part by the U.S. National Science Foundation under Grant ECCS-1308045.

Abstract

Abstract: Maintenance costs account for a significant portion of the total cost of electricity generated by wind turbines. Currently in the wind power industry, maintenance is mainly performed on regular schedules or when significant damage occurs in a wind turbine making it inoperable, instead of being determined by the actual condition of the wind turbine. Among the total maintenance costs, approximately 25%~35% is related to regularly scheduled preventive maintenance and 65%~75% to unscheduled corrective maintenance. To reduce the failure rate and level and maintenance costs and improve the availability, reliability, safety, and lifespans of wind turbines, it is desirable to perform condition-based predictive maintenance for wind turbines, which will require a high-fidelity online prognostic condition monitoring system(CMS) for fault diagnosis and prognosis and remaining useful life(RUL) prediction of wind turbines. Most of the existing wind turbine CMSs are based on vibration monitoring and have no or limited capability in fault prognosis and RUL prediction. Compared to vibration monitoring, the prognostic condition monitoring techniques based on generator current signal analysis proposed recently have significant advantages in terms of cost, hardware complexity, implementation, and reliability. This paper discusses the principles and challenges of using generator current signals for prognostic condition monitoring of wind turbine drivetrains and presents an overview of recent advancements in this area.

Key words: Current signal, drivetrain, fault diagnosis, fault prognosis, prediction, prognostic condition monitoring, remaining useful life(RUL), wind turbine

Wei Qiao, Liyan Qu. Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis[J]. Chinese Journal of Electrical Engineering, 2018, 4(3): 80-89.

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