Name
Title | ||
|---|---|---|
Dependency in State Transitions of Wind Turbines - Inference on Model Residuals for State Abstractions. |
Abstract | ||
|---|---|---|
Abstracting turbine states and predicting the transition into failure states ahead of time is important in operation and maintenance of wind turbines. This study presents a method to monitor state transitions of a wind turbine based on the online inference on residuals. In a Bayesian framework, the state transitions are based on a hidden variable relevant for the predictor, namely the information ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIE.2017.2674580 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Wind turbines,Data models,Monitoring,Bayes methods,Fault detection,Time series analysis | Time series,Data mining,Data modeling,Hyperparameter,Conditional probability,Control theory,Inference,Algorithm,Engineering,Hidden variable theory,Wind power,Bayesian probability | Journal |
Volume | Issue | ISSN |
64 | 6 | 0278-0046 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jürgen Herp | 1 | 0 | 0.34 |
| Mohammad H. Ramezani | 2 | 2 | 2.08 |
| Esmaeil S. Nadimi | 3 | 9 | 5.90 |