Wind energy is the fastest-growing energy source in the world nowadays and most wind turbines are installed at remote areas, e.g. country side, off sea-shore. Having a reliable fault diagnosis and fault tolerant control (FTC) scheme is crucial to improve the reliability of wind turbines and reduce expensive repair cost. This PhD work is motivated by this fact and a model-based fault diagnosis and FTC scheme is developed for a doubly fed induction generator (DFIG) based wind turbine system. In particular, an electrical and a mechanical fault scenarios, the DFIG winding short circuit and drive train faults, are considered due to their high occurrence rates.For the DFIG winding short circuit fault, two mathematical models of DFIG with respect to two types of faults, i.e. single-phase and multi-phase faults, are proposed which can represent all possible cases of the faults. Moreover, the state-space representations of these models are derived by using reference frame transformation theory, such that the faults are represented by some unknown variables or parameters. Based on these models, an adaptive observer based fault diagnosis scheme is proposed to diagnose short circuit faults via online estimation of unknown variables or parameters. By dong this, the fault level and location can be online diagnosed. To consider the effects of model uncertainties, two robust adaptive observers are proposed based on the Hinfinity optimization and high-gain observer techniques, respectively, which can ensure the accuracy and robustness of fault estimations. In addition, a self-scheduled LPV adaptive observer is developed with consideration of rotor speed variations, which is suitable for the fault diagnosis under non-stationary conditions. In the context of FTC, a fault compensator is developed based on fault information provided by the fault diagnosis scheme, and it incorporates with a traditional controller (i.e. stator flux oriented controller) to provide an online fault compensation of winding short circuit faults.For the mechanical drive train fault, the work focuses on FTC rather than diagnosis. Without using an explicit fault diagnosis scheme, an active FTC scheme is directly designed by employing an adaptive input-output linearizing control (AIOLC) technique. It provides a perfect reference tracking of the torque and reactive power no matter whether the fault occurs. In addition, a robust AIOLC is proposed in order to ensure FTC performance against model uncertainties.
|Date of Award||1 Aug 2012|
- The University of Manchester
|Supervisor||Hong Wang (Supervisor)|