Data–Driven Approach for Wind Turbine Actuator and Sensor Fault Detection and Isolation

In order to improve reliability of wind turbines, it is important to detect and isolate faults as fast as possible, and handle them in an optimal way. An important component in modern wind turbines is the converter, which for a wind turbine control point–of–view normally provides the torque acting on the wind turbine generator, as well as measurement of this torque. In this work, a diagnosis strategy based on fuzzy prototypes is presented, in order to detect these faults in the converter, and isolate them either to be an actuator or a sensor fault. The fuzzy system is used since the model under investigation is nonlinear, whilst the wind speed measurement is highly noisy, due to the turbulence around the rotor plane. The fuzzy system consists of a set of piecewise affine Takagi–Sugeno models, which are identified from the noisy measurements acquired from the simulated wind turbine. The fault detection and isolation strategy is thus designed based on these fuzzy models. The wind turbine simulator is finally used to validate the achieved performances of the suggested fault detection and isolation scheme