Fault Parameter Estimation Using Adaptive Fuzzy Fading Kalman Filter

Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is ref...

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Bibliographic Details
Published in:Applied sciences 2019-08, Vol.9 (16), p.3329
Main Authors: Kim, Donggil, Lee, Dongik
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Fading
fault
Fault detection
Fault diagnosis
fuzzy
kalman filter
Kalman filters
Methods
Noise
Parameter estimation
Principal components analysis
Sensors
State estimation
Turbines
Wind power
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Summary:Early detection and diagnosis of wind turbine faults is critical for applying a possible maintenance and control strategy to avoid catastrophic incidents. This paper presents a novel method to estimate the parameter of faults in a wind turbine. In this work, the estimation of fault parameters is reformulated as the state estimation problem by augmenting the parameters as an additional state. The novelty of the proposed method lies in the use of an adaptive fuzzy fading algorithm for the adaptive Kalman filter so that the convergence property during the estimation of fault parameter can be improved. The performance of the proposed method is evaluated through a set of numerical simulations with both linear and non-linear models.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9163329