| Titre : | Monitoring wind turbine vibration based on SCADA data (2012) |
| Auteurs : | Zijun Zhang, Auteur ; Andrew Kusiak, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME. Journal of solar energy engineering (Vol. 134 N° 2, Mai 2012) |
| Article en page(s) : | 12 p. |
| Note générale : | solar energy |
| Langues : | Anglais |
| Index. décimale : | 621.47 |
| Tags : | turbine vibration ; monitoring ; control chart ; k-means clustering ; drivetrain acceleration ; tower date-mining ; neural networks ensemble |
| Résumé : | Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data. |
| DEWEY : | 621.47 |
| ISSN : | 0199-6231 |
| En ligne : | http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JSEEDO000134000002021004000001&idtype=cvips&gifs=Yes&ref=no |
