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Conference proceeding
Fault detection and isolation using redundant sensors for a wind turbine with doubly-fed induction generator (DFIG)
AzCIFToMM International Symposium of Mechanism and Machine Science 2010, pp.232-238
2010
Abstract
Modern wind turbines increase in size, megawatt capacities and their power production to overcome their financial disadvantage. This leads a necessity of more reliable power plants and much effort to enhance the reliability of the wind turbines. That's where Fault Detection and Isolation (FDI) systems become important. The aim of this study is to create a system that detects sensor faults in a redundant speed sensor system which is used on wind turbine with doubly-fed induction generator (DFIG). Fault detection and isolation simulations were performed via Simulink for a linear model of a 2MW wind turbine. Two kinds of faults are considered, fixed value (bias type) and gain factor faults. These faults are assumed to affect the redundant sensor system which measures the angular speed of the generator shaft. The generated residuals were controlled using a statistical approach. A chi-square hypothesis test was performed to achieve the fault detection, and angle comparison was taken into account for isolation. The designed system achieves fault detection and isolation with a prespecified false alarm probability.
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- Title
- Fault detection and isolation using redundant sensors for a wind turbine with doubly-fed induction generator (DFIG)
- Publication Details
- AzCIFToMM International Symposium of Mechanism and Machine Science 2010, pp.232-238
- Resource Type
- Conference proceeding
- Publisher
- Intl Federation for the Promotion of Mechanism and Machine Science ( IFToMM)
- Copyright
- Permission granted to the University of West Florida Libraries to digitize and/or display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder.
- Identifiers
- 99380090771906600
- Academic Unit
- Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English