Model Based Detection for Ice on Wind Turbine Blades

Abstract

Wind power to cold climate sites is attractive because of favorable wind conditions and low population density. However, icing of wind turbine blades remains one of the main challenges for cold climate sites. Ice formation on wind turbine blades causes several problems, such as the loss of power production, unbalanced loads in drive train and ice throw. Most of the existing detection methods need special sensors installed and would be expensive to achieve a satisfied accuracy. In this research, an alternative model-based ice on blade detection method is proposed. There are mainly two advantages of the method. Firstly there are no additional measurements are needed. Secondly the detection method can be implemented for any kind of blade aerodynamic changes, not only ice on blade.
The basis of the mode-based ice-detection method is a reliable linearized wind turbine model. The accuracy of the model is mainly influenced by the number of degrees of freedom (DOFs) and the number of operation points (OP). The most efficient model is the one with less DOFs and OP but without losing much accuracy. The relative importance of DOF and OP is revealed and suggestions are given for an optimum choice of DOF and OP for different quantities. Specifically, for power production properties, increase the number of OP is more efficient. While, for blade and tower related properties, increase the number of DOF is a better choice.
The ice on blade influence of aerodynamic force, power production and structure loads is studied for three ice conditions, which are start ice, light ice and moderate ice. Based on the ice on blade influence and the reliable linearized wind turbine model, the concept of model-based ice-detection is proposed as follows: Firstly ice on blade changes the aerodynamics and the blade mass. Therefore it can be considered as one wind turbine system changed to a different system. That means the outputs of the two systems, with ice and without ice, are different, even the inputs are exactly the same. From the ice-detection point of view, if the difference of outputs between iced system and cleaned one is much larger than modeling error, ice on blade can be detected.
With the proposed model-based detection method, the ice-detection capability of different output quantities is studied. It has been found that the power production related quantities usually have the highest ice-detection capability, while the blade and tower properties have relatively lower capability. In order to verify the ice-detection method for a broad working conditions, implementations are performed for above and below rated wind speed conditions. Successful detections have been achieved for both of them.
For possible measurement errors in reality, they are efficiently considered by introducing uncertainty factors. The ice-detection capabilities with measurement error have been analyzed for three ice conditions and detected based on different quantities. Although a drop in the detection capability has been observed if considering the measurement error, all the quantities still can detect the ice successfully to some degree.