Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data
Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/6/1728 |
| _version_ | 1797540653157056512 |
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| author | Cristian Velandia-Cardenas Yolanda Vidal Francesc Pozo |
| author_facet | Cristian Velandia-Cardenas Yolanda Vidal Francesc Pozo |
| author_sort | Cristian Velandia-Cardenas |
| collection | DOAJ |
| description | Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related to the temperature of the main gearbox of a wind turbine. An imbalanced dataset is a classification data set that contains skewed class proportions (more observations from one class than the other) which can cause a potential bias if it is not handled with caution. Furthermore, the dataset is time dependent introducing an additional variable to deal with when processing and splitting the data. These methods are aimed to reduce false positives and false negatives, and to demonstrate the effectiveness of well-applied preprocessing techniques for improving the performance of different machine learning algorithms. |
| first_indexed | 2024-03-10T13:04:14Z |
| format | Article |
| id | doaj.art-ce118e7e40e34eaaa1a7ed4bfa25712d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:04:14Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ce118e7e40e34eaaa1a7ed4bfa25712d2023-11-21T11:17:21ZengMDPI AGEnergies1996-10732021-03-01146172810.3390/en14061728Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA DataCristian Velandia-Cardenas0Yolanda Vidal1Francesc Pozo2Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, SpainControl, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, SpainControl, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, SpainWind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related to the temperature of the main gearbox of a wind turbine. An imbalanced dataset is a classification data set that contains skewed class proportions (more observations from one class than the other) which can cause a potential bias if it is not handled with caution. Furthermore, the dataset is time dependent introducing an additional variable to deal with when processing and splitting the data. These methods are aimed to reduce false positives and false negatives, and to demonstrate the effectiveness of well-applied preprocessing techniques for improving the performance of different machine learning algorithms.https://www.mdpi.com/1996-1073/14/6/1728fault detectionmachine learningprincipal component analysisSCADAstructural health monitoringwind turbine |
| spellingShingle | Cristian Velandia-Cardenas Yolanda Vidal Francesc Pozo Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data Energies fault detection machine learning principal component analysis SCADA structural health monitoring wind turbine |
| title | Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data |
| title_full | Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data |
| title_fullStr | Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data |
| title_full_unstemmed | Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data |
| title_short | Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data |
| title_sort | wind turbine fault detection using highly imbalanced real scada data |
| topic | fault detection machine learning principal component analysis SCADA structural health monitoring wind turbine |
| url | https://www.mdpi.com/1996-1073/14/6/1728 |
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