Abstract
Accurate prognosis of wind turbine generator failures is essential for reducing operation and maintenance costs in wind farms. Existing methods rely on expensive, purpose-built condition monitoring systems to conduct diagnosis and prognosis of wind turbine generator failures. In this paper, we present a prognosis method to predict the remaining useful life (RUL) of generators, which requires no additional hardware support beyond widely adopted SCADA system. This work first introduces a notion, Anomaly Operation Index (AOI), to quantitatively measure wind turbine performance degradation in runtime. It then presents a data-driven wind turbine anomaly detection method and a time series analysis method to predict the wind turbine generator RUL. Experimental study on real-world wind farm data demonstrates that the proposed methods can achieve accurate prediction of wind turbine generator RUL and provide sufficient lead time for scheduling maintenance and repair.
| Original language | English |
|---|---|
| Title of host publication | 2016 North American Power Symposium (NAPS) |
| Publisher | IEEE |
| ISBN (Electronic) | 978-1-5090-3270-9 |
| ISBN (Print) | 978-1-5090-3271-6 |
| DOIs | |
| Publication status | Published - 21 Nov 2016 |
| Event | 2016 North American Power Symposium - Denver, United States Duration: 18 Sept 2016 → 20 Sept 2016 |
Conference
| Conference | 2016 North American Power Symposium |
|---|---|
| Abbreviated title | NAPS |
| Country/Territory | United States |
| City | Denver |
| Period | 18/09/16 → 20/09/16 |
Funding
This work was supported in part by National Natural Science Foundation of China under Grant No. 61233016 and the International Exchange Program for Graduate Students, Tongji University.
Keywords
- wind turbines
- generators
- Prognostics and health management
- Maintenance engineering
- SCADA systems
- Wind farms
- Temperature measurement
