Wind turbine ice detection using hyperspectral imaging

Abstract

Wind energy has been playing a pivot role in replacing the traditional energy sources. This emerging paradigm has proved itself as a good candidate among all renewable energy sources. Although the exponential growth of the wind industry, wind turbines still suffer from blade icing especially in cold regions. Blade icing disturbs aerodynamic performance and results in power losses, safety risks, mechanical and electrical breakdowns, measurement, and control faults. Anti-icing and de-icing techniques mitigate these adverse effects. It is mandatory to rigorously evaluate the meteorological operating conditions during the assessment phase to determine the need and advantages of installing an anti-icing or a de-icing system. Moreover, this diagnostic is also essential during the operation to detect icing, prevent failure, and enhance production. Different ice detection methods, such as double anemometry, vane, relative humidity, and dew point, are used. These techniques have few drawbacks that can be overcome using hyperspectral imaging. This paper offers an overview of icing detection technologies and explores spectroscopy imaging applications for detecting ice accretion in wind farms. This study describes the application of this non-destructive and fast monitoring technique in remote sensing of icing incident on a wind turbine blade. This paper outlines the experimental approach conducted on a blade sample with an ice-covered portion. The icing model, on which this detection method is based, is designed, simulated, and confirmed to acquire enhanced blade icing knowledge. The hyperspectral imaging validation results for icing occurrence detection in their initial development phases are satisfactory. The experimental findings of this technique reveal that the accuracy and precision of blade icing detection are considerably enhanced.