Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data
Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/11/3018 |
| _version_ | 1811305646128103424 |
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| author | Yolanda Vidal Francesc Pozo Christian Tutivén |
| author_facet | Yolanda Vidal Francesc Pozo Christian Tutivén |
| author_sort | Yolanda Vidal |
| collection | DOAJ |
| description | Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines. |
| first_indexed | 2024-04-13T08:30:11Z |
| format | Article |
| id | doaj.art-836fd38f7f54440ba02a395bc029d3d5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:30:11Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-836fd38f7f54440ba02a395bc029d3d52022-12-22T02:54:17ZengMDPI AGEnergies1996-10732018-11-011111301810.3390/en11113018en11113018Wind Turbine Multi-Fault Detection and Classification Based on SCADA DataYolanda Vidal0Francesc Pozo1Christian Tutivén2Control, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, SpainControl, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, SpainControl, Modeling, Identification and Applications (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, SpainDue to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.https://www.mdpi.com/1996-1073/11/11/3018wind turbinefault detectionfault classificationfault diagnosisprincipal component analysissupport vector machines(Fatigue, Aerodynamics, Structures and Turbulence) FAST code |
| spellingShingle | Yolanda Vidal Francesc Pozo Christian Tutivén Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data Energies wind turbine fault detection fault classification fault diagnosis principal component analysis support vector machines (Fatigue, Aerodynamics, Structures and Turbulence) FAST code |
| title | Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data |
| title_full | Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data |
| title_fullStr | Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data |
| title_full_unstemmed | Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data |
| title_short | Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data |
| title_sort | wind turbine multi fault detection and classification based on scada data |
| topic | wind turbine fault detection fault classification fault diagnosis principal component analysis support vector machines (Fatigue, Aerodynamics, Structures and Turbulence) FAST code |
| url | https://www.mdpi.com/1996-1073/11/11/3018 |
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