1. 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

2. 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

3. 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):

4. 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):

5. 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.

6. 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.

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

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

9. 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.

10. 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.

11. Angular imaging Detector10 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.

12. 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

13. 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.

14. 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,
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,
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:

15. Thank you