Wind turbine database for intelligent operation and maintenance strategies
With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5¿MW wind turbines is presented. The database contains 312 analogous variables recor...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/404673 |
| Acceso en línea: | https://hdl.handle.net/2117/404673 https://dx.doi.org/10.1038/s41597-024-03067-9 |
| Access Level: | acceso abierto |
| Palabra clave: | Wind turbines Maintainability (Engineering) Data transmission systems System failures (Engineering) Failure analysis (Engineering) Aerogeneradors Mantenibilitat (Enginyeria) Dades--Transmissió Avaries Anàlisi de fallades (Enginyeria) Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors Àrees temàtiques de la UPC::Informàtica::Automàtica i control |
| Sumario: | With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5¿MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value. |
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