Wind Turbine Blade Icing Monitoring, De-Icing and Wear Prediction Device Based on Machine Vision Recognition Algorithm

Icing of wind turbine blades will lead to blade wear, load increase and affect power generation. In this paper, machine vision monitoring is the main focus, and laser displacement sensor is used as a supplement, so as to obtain the ice condition on the surface of wind turbine blades. And it determines whether de-icing operation is needed through the microcontroller. If the pre-set de-icing threshold is reached, the acoustic de-icing system generates waves with different ratios to de-ice, and the real-time icing images collected from the blades can be uploaded to the NB-IoT node through the wireless communication module, and then uploaded to the cloud server for big data mining and analysis, and the visualization page of the wind turbine central control room will also be presented synchronously. The cloud server will be uploaded to the NB-IoT node and then uploaded to the cloud server for big data mining and analysis, and the visualization page in the control room of the wind turbine will also be presented synchronously. The large database related to blade icing prediction can provide the basis for the prediction and analysis model of wind turbine blade wear and icing conditions. The integration of wind turbine blade icing detection and de-icing enables wind turbines to work efficiently and meets the current requirements of energy saving and emission reduction and market demand.