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Bank of Extended Kalman Filters for Faults Diagnosis in Wind Turbine Doubly Fed Induction Generator

Imane Idrissi 1, 2 Houcine Chafouk 2 Rachid El Bachtiri 1 Maha Khanfara 1
2 Pôle Automatique et Systèmes
IRSEEM - Institut de Recherche en Systèmes Electroniques Embarqués
Abstract : In order to increase the efficiency, to ensure availability and to prevent unexpected failures of the doubly fed induction generator (DFIG), widely used in speed variable wind turbine (SVWT), a model based approach is proposed for diagnosing stator and rotor winding and current sensors faults in the generator. In this study, the Extended Kalman Filter (EKF) is used as state and parameter estimation method for this model based diagnosis approach. The generator windings faults and current instruments defects are modelled, detected and isolated with the use of the faults indicators called residuals, which are obtained based on the EKF observer. The mathematical model of DFIG for both healthy and faulty operating conditions is implemented in Matlab/Simulink software. The obtained simulation results demonstrate the effectiveness of the proposed technique for diagnosis and quantification of the faults under study
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02418847
Contributor : Houcine Chafouk <>
Submitted on : Thursday, December 19, 2019 - 10:47:46 AM
Last modification on : Monday, September 14, 2020 - 2:41:54 PM

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Imane Idrissi, Houcine Chafouk, Rachid El Bachtiri, Maha Khanfara. Bank of Extended Kalman Filters for Faults Diagnosis in Wind Turbine Doubly Fed Induction Generator. Telkomnika (Telecommunication, Computing, Electronics and Control), Institute of Advanced Engineering and Science (IAES), 2018, Journal of Telkomnika, 16 (6), ⟨10.12928/telkomnika.v16i6⟩. ⟨hal-02418847⟩

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