More and more wind turbines are installed in cold regions because of better wind resources. In these regions, the high humidity and low temperatures in winter will lead to ice accumulation on the wind turbine impeller. A different icing location or mass will lead to different natural frequency variations of the impeller. In order to monitor the icing situation in time and in advance, a method based on depth neural network technology to predict the icing mass is explored and proposed. Natural-environment icing experiments and iced-impeller modal experiments are carried out, aiming at a 600 W wind turbine, respectively. The mapping relationship between the change rate of the natural frequency of the iced impeller at different icing positions and the icing mass is obtained, and the correlation coefficients are all above 0.93. A deep neural network (DNN) prediction model of ice-coating quality for the impeller was constructed with the change rate of the first six-order natural frequencies as the input factor. The results show that the MAE and MSE of the trained model are close to 0. The average prediction error of the DNN model is 4.79%, 9.35%, 3.62%, 1.63%, respectively, under different icing states of the impeller. It can be seen that the DNN shows the best prediction ability among other methods. The smaller the actual ice-covered mass of the impeller, the larger the relative error of the ice-covered mass predicted by the DNN model. In the same ice-covered state, the relative error will decrease gradually with the increase in ice-covered mass. In a word, using the natural frequency change rate to predict the icing quality is feasible and accurate. The research achievements shown here can provide a new idea for wind farms to realize efficient and intelligent icing monitoring and prediction, provide engineering guidance for the wind turbine blade anti-icing and deicing field, and further reduce the negative impact of icing on wind power generation.

The icing of wind turbine blades can cause changes in airfoil shape, which in turn significantly reduces the aerodynamic performance and affects the power generation efficiency of a wind turbine. In this paper, the iced airfoil shape of wind turbine blades with different positions, masses, and angles of attack icing was measured and modeled using 3D scanning technology, and changes in airfoil shape parameters under different icing conditions were obtained. The numerical simulations of icing blades were carried out to investigate the effect of blade icing on aerodynamic characteristics. The results show that ice accumulation thickness tends to increase nonlinearly along the spanwise direction and chord length for both windward and leeward icing. The airfoil angle of attack affects the trend of ice accumulation changes. As shown by the numerical simulation of the aerodynamic characteristic, blade icing changes the airfoil shape, which changes the pressure difference between the leading edge and trailing edge, affects the size and number of the wake vortex structures, and further changes the aerodynamic characteristics of the blade.