Journal
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Volume 53, Issue 2, Pages 878-887Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2016.2628362
Keywords
Current signal; doubly-fed induction generator (DFIG); fault detection; fault identification; fusion; gearbox; support vector machine (SVM); wind turbine
Funding
- Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy [DE-EE0006802]
- U.S. National Science Foundation [ECCS-1308045]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1308045] Funding Source: National Science Foundation
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This paper proposes a new fault detection and identification framework for drivetrain gearboxes of wind turbines equipped with doubly-fed induction generators (DFIGs) based on the fusion of DFIG stator and rotor current signals. First, the characteristic frequencies of gearbox faults in DFIG stator and rotor currents are analyzed. Different time-and frequency-domain features of gearbox faults in DFIG stator and rotor current signals are then defined, and the methods to extract these features are introduced. These features are used as the inputs of multiclass support vector machines with probabilistic outputs for fault mode identification. Different schemes that use a single stator or rotor current signal or both stator and rotor current signals for the feature-or decision-level information fusion are designed. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to validate the proposed current-based fault detection and identification framework.
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