Detection and classification of snow/ice using infrared imaging PhD student: Lavan Kumar Eppanapelli Supervisors: Mikael Sjödahl Johan Casselgren Division of Fluid and Experimental Mechanics Luleå university of technology, Luleå Sweden

Problem statement Cold climate often leads to Complete production loss Reduction of power Overloading Increased fatigue Safety issues There are however techniques to prevent the icing events such as anti-icing and de-icing techniques. More details of these techniques are shown in next slide. Jasinski, William, et al. "Wind turbine performance under icing conditions." 35th Aerospace Sciences Meeting and Exhibit. 1997.

Problem statement (cont.) Anti-icing methods prevent ice accretion Ice phobic coatings Black paint Chemicals De-icing systems remove/melt the ice of the blades Electrical heating element Warm air and radiator Flexible pneumatic boots Most manufacturers use epoxy or polyester matrix composites reinforced with glass or carbon fibres. Current research heading towards Nano composite coatings . Parent, Olivier, and Adrian Ilinca. "Anti-icing and de-icing techniques for wind turbines: Critical review." Cold regions science and technology 65.1 (2011): 88-96.

Available techniques Several techniques are available to address the presented problem statement. These techniques based on two methods such as direct detection and indirect detection. Direct detection sensors work based on some property change such as reflectance, where mass, conductivity and inductance etc., Indirect detection sensors work based on weather conditions and power curves of turbine. The presented technique relates to direct detection, where variations of intensity of the reflected light from surfaces is investigated. Homola, Matthew C., Per J. Nicklasson, and Per A. Sundsbø. "Ice sensors for wind turbines." Cold regions science and technology 46.2 (2006): 125-131.

Objective Developing an experimental system that can be used to reliably detect snow/ice be able to classify different types of snow/ice This is done, by imaging light interaction with a wind turbine blade surface during icing/melting events Reliable detection of snow/ice to optimize the de-icing system Classification of snow/ice provides additional information for example to simulate/model ice load and ice accretion on the turbine blades.

Approach-I Here, experimental setup to characterize snow/ice according to their physical properties is investigated. Intensity of reflected light is measured at several angles and several bands of wavelengths to find optimal wavelength bands where the classification and characterization is best possible.

Icy blade Three spots, 980 nm, 1310 nm and 1550 nm Camera Laser diodes 980 nm 1310 nm 1550 nm Melting stage Blade

A wing was iced and recorded the video of melting process over time 980 nm 1550 nm 1310 nm

Results (cont.,) Fig 9: Visualization of classification based on RGB concept. Intensity values at three wavelength bands were converted into a RGB matrix based on some normalizing calculations. One can observe from Figure 9 that color coded interpretation of each icing condition is possible. For example, wet condition shows black, which is actually means that all the light is being absorbed.

Approach-II Here, experimental setup to characterize different phases of water on a piece of wind turbine blade is presented. Laser diodes of three wavelength bands 980 nm, 1310 nm and 1550 nm, were used as a light source and the NIR camera used as a detector. The experimental setup for these measurements is similar to Figure 3.

Angular imaging Detector 10 20 30 40 50 70 80 -10 -20 -30 -40 -50 -60 -70 -80 60 -50 60 Fig 1: Illustration of angular imaging of intensity of reflected light from snow/ice surface.

Results 1550 nm 980 nm 1310 nm Angle Forward Fig 5: Classification of snow types with different physical properties Samples with individual grains are at one place, and possible to distinguish themselves too. Samples with higher density are observed to be at one place. It was also observed that there is a linear correlation between the coefficients and density . Similar snow with slightly different surface texture is also distinguishable from other samples

Conclusions The approach can be used to Classify snow /ice with different physical properties Grain structure Density Surface texture Classify different phases of water on a piece of wind turbine blade An illustration of color scale shows that the technique is promising.

Summary Publications: Conferences: Licentiate: Eppanapelli, L. K., Friberg, B., Casselgren, J., & Sjödahl, M. (2016). Estimation of a low-order Legendre expanded phase function of snow. Optics and Lasers in Engineering, 78, 174-181. Eppanapelli, L. K., Casselgren, J., Wåhlin, J., & Sjödahl, M. (2017). Investigation of snow single scattering properties based on first order Legendre phase function. Optics and Lasers in Engineering, 91, 151-159. Eppanapelli, L. K., Casselgren, J., & Wåhlin, J. (2016). Classification of snow types based on spectral reflectance characteristics in NIR region. Submitted for publication in Cold Regions Science and Technology. Eppanapelli, L. K., Lintzen, N., Casselgren, J., & Wåhlin, J. (2016). Liquid content in snow measured by spectral reflectance. Submitted for publication in Cold Regions Engineering. Conferences: Eppanapelli, L. K., Remote detection of phases of water on a wind turbine blade, presented at Winterwind 2015, Feb 2015 Eppanapelli, L. K., Characterizing different types of snow/ice on a turbine blade using multispectral imaging, presented at EAWE PhD seminar, Apr 2016 Licentiate: Eppanapelli, L. K., Classification of different types of snow using spectral and angular imaging, Luleå University of Technology, Institutionen för teknikvetenskap och matematik, ORCID-id: 0000-0002-5943-1476.

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