Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders
A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/23/8649 |
| _version_ | 1797545833298657280 |
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| author | Mattia Beretta Juan José Cárdenas Cosmin Koch Jordi Cusidó |
| author_facet | Mattia Beretta Juan José Cárdenas Cosmin Koch Jordi Cusidó |
| author_sort | Mattia Beretta |
| collection | DOAJ |
| description | A hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous patterns within the input variables. Alarm logs are processed and merged to the anomaly detection output, creating a reliable health estimator of generator conditions. The proposed methodology has been tested on a fleet of 115 wind turbines from four different manufacturers located in various locations around Europe. The solution has been compared with other existing data modeling techniques offering impressive results on the fleet. An accuracy of 82% and a Kappa of 56% were obtained. The detailed methodology is presented using one of the available windfarms, composed of 13 onshore wind turbines rated 2 MW power. The rigorous evaluation of the results, the utilization of real data and the heterogeneity of the dataset prove the validity of the system and its applicability in an online operating scenario. |
| first_indexed | 2024-03-10T14:21:37Z |
| format | Article |
| id | doaj.art-a5f247b44e494d0fa3db69518383b542 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:21:37Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a5f247b44e494d0fa3db69518383b5422023-11-20T23:21:27ZengMDPI AGApplied Sciences2076-34172020-12-011023864910.3390/app10238649Wind Fleet Generator Fault Detection via SCADA Alarms and AutoencodersMattia Beretta0Juan José Cárdenas1Cosmin Koch2Jordi Cusidó3Unitat Transversal de Gestió de l’Àmbit de Camins UTGAC, Universitat Politécnica de Catalunya, 08034 Barcelona, SpainSMARTIVE S.L., 08204 Sabadell, SpainSMARTIVE S.L., 08204 Sabadell, SpainSMARTIVE S.L., 08204 Sabadell, SpainA hybrid health monitoring system for wind turbine generators is introduced. The novelty of this research consists in approaching a 115-wind turbine fleet by using the fusion of multiple sources of information. Analog SCADA data is analyzed through an autoencoder which allows to identify anomalous patterns within the input variables. Alarm logs are processed and merged to the anomaly detection output, creating a reliable health estimator of generator conditions. The proposed methodology has been tested on a fleet of 115 wind turbines from four different manufacturers located in various locations around Europe. The solution has been compared with other existing data modeling techniques offering impressive results on the fleet. An accuracy of 82% and a Kappa of 56% were obtained. The detailed methodology is presented using one of the available windfarms, composed of 13 onshore wind turbines rated 2 MW power. The rigorous evaluation of the results, the utilization of real data and the heterogeneity of the dataset prove the validity of the system and its applicability in an online operating scenario.https://www.mdpi.com/2076-3417/10/23/8649alarmsanomaly detectionautoencoderfault detectionSCADA datagenerator |
| spellingShingle | Mattia Beretta Juan José Cárdenas Cosmin Koch Jordi Cusidó Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders Applied Sciences alarms anomaly detection autoencoder fault detection SCADA data generator |
| title | Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders |
| title_full | Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders |
| title_fullStr | Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders |
| title_full_unstemmed | Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders |
| title_short | Wind Fleet Generator Fault Detection via SCADA Alarms and Autoencoders |
| title_sort | wind fleet generator fault detection via scada alarms and autoencoders |
| topic | alarms anomaly detection autoencoder fault detection SCADA data generator |
| url | https://www.mdpi.com/2076-3417/10/23/8649 |
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