Simultaneous Reconstruction of Actuator and Sensor Faults in PMSG-Based Wind Turbines Under Various Anomaly Profiles: A Takagi–Sugeno Fuzzy Observer Approach

This paper addresses the simultaneous estimation of actuator and sensor faults in a permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS) intended for isolated microgrids. The nonlinear dynamics of the wind turbine are represented by a Takagi–Sugeno (T–S) fuzzy model whose premise variable, the generator speed, is not measured; only the stator currents are measured. The faults, modeled as signals with bounded second derivatives, are treated as unknown inputs through an augmented state obtained by filtering the measured output, and an augmented fuzzy proportional–integral (PI) observer is adapted to this configuration. The structural conditions for reconstruction are stated and verified. The membership-function mismatch, which multiplies the true augmented state, is treated as a bounded input, whose bound is based on the differences between local matrices. Sufficient linear matrix inequality (LMI) conditions guarantee the uniform ultimate boundedness of the estimation error, with a prescribed decay rate and an L2 attenuation level. The approach is evaluated in simulation, under a persistent measurement disturbance, for simultaneous actuator and sensor faults and then for biases, drifts and transients on each channel. The unmeasured speed is reconstructed with a root mean square error (RMSE) below 0.1% of its operating value and converges in about 2 s; the simultaneous faults are reconstructed with RMSEs of about 0.2% (fa) and 3% (fs) of their amplitudes, and the empirical L2 gain, at most 0.92, remains below the attenuation level of 1.875 obtained from the LMI conditions.