Name
Abstract | ||
|---|---|---|
The great growth of wind power plants has drawn much attention to operations and maintenance problems. Maintenance in a wind turbine is of paramount importance to minimize potential problems. This work presents a methodology for predicting the behavior of wind turbines based on some data. The prediction was made through the technique of ANNs (Artificial Neural Networks) and a R2 (coefficient of determination) for the adjustment of the best behavior of the data. The database used is a model of a manufacturer of wind turbines called ENERCON, the research is of the exploratory type. Predicted information is essential to assist in preventive maintenance if any anomaly occurs according to the technique. The models used generated good results. |
Year | Venue | Field |
|---|---|---|
2018 | WorldCIST | Computer science,Turbine,Artificial neural network,Preventive maintenance,Wind power,Reliability engineering,Data prediction |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darielson A. Souza | 1 | 0 | 0.34 |
| Alanio F. Lima | 2 | 0 | 0.34 |
| Adriano P. Maranhão | 3 | 0 | 0.34 |
| Thiago P. Maranhão | 4 | 0 | 0.34 |
| Luís O. N. Teles | 5 | 0 | 0.34 |
| Flavio R. S. Nunes | 6 | 0 | 0.34 |
| Jefferson J. I. Souza | 7 | 0 | 0.34 |
| Antonio T. S. Brito | 8 | 0 | 0.34 |