The installed energy generation capacity of wind turbines is increasing dramatically on a global scale; this means that reliability of wind turbines is of higher importance. A part of this task is to improve fault detection and accommodation schemes of the wind turbine. This paper presents a benchmark model for simulation of fault detection and accommodation schemes. This benchmark model deals with the wind turbine on a system level containing sensors, actuators and systems faults in the pitch system, drive train, generator and converter system.

The installed energy generation capacity of wind turbines is increasing dramatically on a global scale; this means that reliability of wind turbines is of higher importance. A part of this task is to improve fault detection and accommodation schemes of the wind turbine. This paper presents a benchmark model for simulation of fault detection and accommodation schemes. This benchmark model deals with the wind turbine on a system level containing sensors, actuators and systems faults in the pitch system, drive train, generator and converter system.

a b s t r a c tHigh performance and reliability are required for wind turbines to be competitive within the energy market. To capture their nonlinear behavior, wind turbines are often modeled using parameter-varying models. In this paper we design and compare multiple linear parameter-varying (LPV) controllers, designed using a proposed method that allows the inclusion of both faults and uncertainties in the LPV controller design. We specifically consider a 4.8 MW, variable-speed, variable-pitch wind turbine model with a fault in the pitch system.We propose the design of a nominal controller (NC), handling the parameter variations along the nominal operating trajectory caused by nonlinear aerodynamics. To accommodate the fault in the pitch system, an active fault-tolerant controller (AFTC) and a passive fault-tolerant controller (PFTC) are designed. In addition to the nominal LPV controller, we also propose a robust controller (RC). This controller is able to take into account model uncertainties in the aerodynamic model.The controllers are based on output feedback and are scheduled on an estimated wind speed to manage the parameter-varying nature of the model. Furthermore, the AFTC relies on information from a fault diagnosis system.The optimization problems involved in designing the PFTC and RC are based on solving bilinear matrix inequalities (BMIs) instead of linear matrix inequalities (LMIs) due to unmeasured parameter variations. Consequently, they are more difficult to solve. The paper presents a procedure, where the BMIs are rewritten into two necessary LMI conditions, which are solved using a two-step procedure.Simulation results show the performance of the LPV controllers to be superior to that of a reference controller designed based on classical principles.

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The wind speed has a huge impact on the dynamic response of wind turbine. Because of this, many control algorithms use a measure of the wind speed to increase performance, e.g. by gain scheduling and feed forward. Unfortunately, no accurate measurement of the effective wind speed is online available from direct measurements, which means that it must be estimated in order to make such control methods applicable in practice. In this paper a new method is presented for the estimation of the effective wind speed. First, the rotor speed and aerodynamic torque are estimated by a combined state and input observer. These two variables combined with the measured pitch angle is then used to calculate the effective wind speed by an inversion of a static aerodynamic model.