Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data
Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/17/3396 |
| _version_ | 1828393257605791744 |
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| author | Mingzhu Tang Wei Chen Qi Zhao Huawei Wu Wen Long Bin Huang Lida Liao Kang Zhang |
| author_facet | Mingzhu Tang Wei Chen Qi Zhao Huawei Wu Wen Long Bin Huang Lida Liao Kang Zhang |
| author_sort | Mingzhu Tang |
| collection | DOAJ |
| description | Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response. |
| first_indexed | 2024-12-10T07:35:58Z |
| format | Article |
| id | doaj.art-160da1d64b334650aacb0d3c727c7682 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-12-10T07:35:58Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-160da1d64b334650aacb0d3c727c76822022-12-22T01:57:25ZengMDPI AGEnergies1996-10732019-09-011217339610.3390/en12173396en12173396Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA DataMingzhu Tang0Wei ChenQi Zhao1Huawei WuWen LongBin Huang2Lida Liao3Kang Zhang4School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China 2 Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China 3 College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA 4 Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou 550004, China 5 School of Engineering, University of South Australia, Adelaide, SA 5095, AustraliaSchool of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China 2 Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China 3 College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA 4 Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou 550004, China 5 School of Engineering, University of South Australia, Adelaide, SA 5095, AustraliaSchool of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China 2 Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China 3 College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA 4 Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou 550004, China 5 School of Engineering, University of South Australia, Adelaide, SA 5095, AustraliaSchool of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China 2 Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China 3 College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA 4 Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou 550004, China 5 School of Engineering, University of South Australia, Adelaide, SA 5095, AustraliaSchool of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410114, China 2 Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang 441053, China 3 College of Engineering, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA 4 Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, Guizhou 550004, China 5 School of Engineering, University of South Australia, Adelaide, SA 5095, AustraliaFault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response.https://www.mdpi.com/1996-1073/12/17/3396fault diagnosisgearboxwind turbinesupport vector regressionsupervisory control and data acquisition system (SCADA) data |
| spellingShingle | Mingzhu Tang Wei Chen Qi Zhao Huawei Wu Wen Long Bin Huang Lida Liao Kang Zhang Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data Energies fault diagnosis gearbox wind turbine support vector regression supervisory control and data acquisition system (SCADA) data |
| title | Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data |
| title_full | Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data |
| title_fullStr | Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data |
| title_full_unstemmed | Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data |
| title_short | Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data |
| title_sort | development of an svr model for the fault diagnosis of large scale doubly fed wind turbines using scada data |
| topic | fault diagnosis gearbox wind turbine support vector regression supervisory control and data acquisition system (SCADA) data |
| url | https://www.mdpi.com/1996-1073/12/17/3396 |
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