Bank of Extended kalman filters for faults diagnosis in wind turbine doubly fed induction generator

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.