Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data

Mingzhu Tang, Wei Chen, Qi Zhao, Huawei Wu, Wen Long, Bin Huang, Lida Liao and Kang Zhang
Additional contact information
Mingzhu Tang: School 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, Australia
Qi Zhao: School 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, Australia
Bin Huang: School 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, Australia
Lida Liao: School 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, Australia
Kang Zhang: School 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, Australia

Energies, 2019, vol. 12, issue 17, 1-15

Abstract: 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.

Keywords: fault diagnosis; gearbox; wind turbine; support vector regression; supervisory control and data acquisition system (SCADA) data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/17/3396/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/17/3396/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:17:p:3396-:d:263662

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().