Abstract
Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.
Original language | American English |
---|---|
Number of pages | 9 |
DOIs | |
State | Published - Jun 2020 |
Event | 2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 - Detroit, United States Duration: 8 Jun 2020 → 10 Jun 2020 |
Conference
Conference | 2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 |
---|---|
Country/Territory | United States |
City | Detroit |
Period | 8/06/20 → 10/06/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.
NREL Publication Number
- NREL/CP-2C00-76575
Keywords
- Generator bearing failures
- Machine learning
- Prediction
- Wind turbine