Decoupling Adaptive Sliding Mode Observer Design for Wind Turbines Subject to Simultaneous Faults in Sensors and Actuators

This paper proposes an adaptive sliding mode observer(ASMO)-based approach for wind turbines subject to simultaneous faults in sensors and actuators.The proposed approach enables the simultaneous detection of actuator and sensor faults without the need for any redundant hardware components.Additionally,wind speed variations are considered as unknown disturbances,thus eliminating the need for accurate measurement or estimation.The proposed ASMO enables the accurate estimation and reconstruction of the descriptor states and disturbances.The proposed design implements the principle of separation to enable the use of the nominal controller during faulty conditions.Fault tolerance is achieved by implementing a signal correction scheme to recover the nominal behavior.The performance of the proposed approach is validated using a 4.8 MW wind turbine benchmark model subject to various faults.Monte-Carlo analysis is also carried out to further evaluate the reliability and robustness of the proposed approach in the presence of measurement errors.Simplicity,ease of implementation and the decoupling property are among the positive features of the proposed approach.

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.