Abstract
In this paper a set-membership approach for fault detection of a benchmark wind turbine is proposed. The benchmark represents relevant fault scenarios in the control system, including sensor, actuator and system faults. In addition we also consider parameter uncertainties and uncertainties on the torque coefficient. High noise on the wind speed measurement, nonlinearities in the aerodynamic torque and uncertainties on the parameters make fault detection a challenging problem. We use an effective wind speed estimator to reduce the noise on the wind speed measurements. A set-membership approach is used generate a set that contains all states consistent with the past measurements and the given model of the wind turbine including uncertainties and noise. This set represents all possible states the system can be in if not faulty. If the current measurement is not consistent with this set, a fault is detected. For representation of these sets we use zonotopes and for modeling of uncertainties we use matrix zonotopes, which yields a computationally efficient algorithm. The method is applied to the wind turbine benchmark problem without and with uncertainties. The result demonstrates the effectiveness of the proposed method compared to other proposed methods applied to the same problem. An advantage of the proposed method is that there is no need for threshold design, and it does not produce positive false alarms. In the case where uncertainty on the torque lookup table is introduced, some faults are not detectable. Previous research has not addressed this uncertainty. The method proposed here requires equal or less detection time than previous results.
Highlights
In recent years, there is an increasing attention to wind energy as one of the most promising and abundant sources for sustainable energy
The method is based on the state space set-membership consistency test
Instead of using wind speed measurement, we use effective wind speed estimation and using interval analysis, the aerodynamic torque which is nonlinear is over-approximated by an interval
Summary
There is an increasing attention to wind energy as one of the most promising and abundant sources for sustainable energy. A model-based method for sensor fault detection of a DFIG is proposed in [10]. A benchmark model for fault detection, isolation and accommodation of wind turbines was proposed in [14]. In this paper we propose a state space based set-membership fault detection method for fault detection of the benchmark model.
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