This paper proposes a robust fault diagnosis scheme based on modified sliding mode observer, which reconstructs wind turbine hydraulic pitch actuator faults as well as simultaneous sensor faults. The wind turbine under consideration is a 4.8 MW benchmark model developed by Aalborg University and kk-electronic a/s. Rotor rotational speed, generator rotational speed, blade pitch angle and generator torque have different order of magnitudes. Since the dedicated sensors experience faults with quite different values, simultaneous fault reconstruction of these sensors is a challenging task. To address this challenge, some modifications are applied to the classic sliding mode observer to realize simultaneous fault estimation. The modifications are mainly suggested to the discontinuous injection switching term as the nonlinear part of observer. The proposed fault diagnosis scheme does not require know the exact value of nonlinear aerodynamic torque and is robust to disturbance/modelling uncertainties. The aerodynamic torque mapping, represented as a two-dimensional look up table in the benchmark model, is estimated by an analytical expression. The pitch actuator low pressure faults are identified using some fault indicators. By filtering the outputs and defining an augmented state vector, the sensor faults are converted to actuator faults. Several fault scenarios, including the pitch actuator low pressure faults and simultaneous sensor faults, are simulated in the wind turbine benchmark in the presence of measurement noises. Simulation results show that the modified observer immediately and faithfully estimates the actuator faults as well as simultaneous sensor faults with different order of magnitudes.
Sliding mode observer based position self-sensing control of a direct-drive PMSG wind turbine system fed by NPC converters
