Wind turbine database for intelligent operation and maintenance strategies
Abstract 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 variab...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03067-9 |
| _version_ | 1827328699108687872 |
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| author | Pere Marti-Puig Alejandro Blanco-M. Jordi Cusidó Jordi Solé-Casals |
| author_facet | Pere Marti-Puig Alejandro Blanco-M. Jordi Cusidó Jordi Solé-Casals |
| author_sort | Pere Marti-Puig |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-07T15:21:05Z |
| format | Article |
| id | doaj.art-930f35ed5059417fbee6d43e6ef76b00 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-07T15:21:05Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-930f35ed5059417fbee6d43e6ef76b002024-03-05T17:39:46ZengNature PortfolioScientific Data2052-44632024-02-0111111310.1038/s41597-024-03067-9Wind turbine database for intelligent operation and maintenance strategiesPere Marti-Puig0Alejandro Blanco-M.1Jordi Cusidó2Jordi Solé-Casals3Data and Signal Processing Research Group, University of Vic–Central University of CataloniaData and Signal Processing Research Group, University of Vic–Central University of CataloniaData and Signal Processing Research Group, University of Vic–Central University of CataloniaData and Signal Processing Research Group, University of Vic–Central University of CataloniaAbstract 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.https://doi.org/10.1038/s41597-024-03067-9 |
| spellingShingle | Pere Marti-Puig Alejandro Blanco-M. Jordi Cusidó Jordi Solé-Casals Wind turbine database for intelligent operation and maintenance strategies Scientific Data |
| title | Wind turbine database for intelligent operation and maintenance strategies |
| title_full | Wind turbine database for intelligent operation and maintenance strategies |
| title_fullStr | Wind turbine database for intelligent operation and maintenance strategies |
| title_full_unstemmed | Wind turbine database for intelligent operation and maintenance strategies |
| title_short | Wind turbine database for intelligent operation and maintenance strategies |
| title_sort | wind turbine database for intelligent operation and maintenance strategies |
| url | https://doi.org/10.1038/s41597-024-03067-9 |
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