Wind turbines are complex dynamic systems forced by stochastic wind disturbances, gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics can be nonlinear and unsteady, wind turbine modelling is thus challenging. Accurate models should thus contain many degrees of freedom to capture the most important dynamic effects. Moreover, wind turbine systems are remotely-installed structures which are also subject to many possible faults. Early fault detection, isolation and successful controller reconfiguration can considerably increase the performance in faulty conditions and prevent abysmal failures in the system. Therefore, the design of fault tolerant control algorithms for wind turbines must account for both complexity and faults. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy. The main purpose of this study is thus to give two examples of viable and straightforward control designs with application to a wind turbine prototype. In particular, the first proposed strategy relies on a fuzzy modelling and identification approach oriented to the design of a passive fault tolerant fuzzy controller. This strategy has been suggested since it is quite simple and easy to implement with respect to different strategies proposed in literature. On the other hand, the second strategy represents an active fault tolerant control scheme relying on adaptive controllers designed by means of the on–line identification of the system model under diagnosis. Extensive simulations on the wind turbine process are the tools for assessing experimentally the reliability, the robustness, and the stability properties of the proposed control schemes in the presence of modelling and measurement errors. These developed control methods are also compared with other different approaches, in order to evaluate advantages and drawbacks of the considered techniques.
Data-Driven Active and Passive Fault Tolerant Control Applications to a Wind Turbine Model
SIMANI, Silvio;
2012
Abstract
Wind turbines are complex dynamic systems forced by stochastic wind disturbances, gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics can be nonlinear and unsteady, wind turbine modelling is thus challenging. Accurate models should thus contain many degrees of freedom to capture the most important dynamic effects. Moreover, wind turbine systems are remotely-installed structures which are also subject to many possible faults. Early fault detection, isolation and successful controller reconfiguration can considerably increase the performance in faulty conditions and prevent abysmal failures in the system. Therefore, the design of fault tolerant control algorithms for wind turbines must account for both complexity and faults. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy. The main purpose of this study is thus to give two examples of viable and straightforward control designs with application to a wind turbine prototype. In particular, the first proposed strategy relies on a fuzzy modelling and identification approach oriented to the design of a passive fault tolerant fuzzy controller. This strategy has been suggested since it is quite simple and easy to implement with respect to different strategies proposed in literature. On the other hand, the second strategy represents an active fault tolerant control scheme relying on adaptive controllers designed by means of the on–line identification of the system model under diagnosis. Extensive simulations on the wind turbine process are the tools for assessing experimentally the reliability, the robustness, and the stability properties of the proposed control schemes in the presence of modelling and measurement errors. These developed control methods are also compared with other different approaches, in order to evaluate advantages and drawbacks of the considered techniques.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
