Monitoring Ice Accumulation and Active De-icing Control of Wind Turbine Blades

  • Shervin Shajiee
  • Lucy Y. PaoEmail author
  • Robert R. McLeod
Part of the Advances in Industrial Control book series (AIC)


Ice accumulation on wind turbines operating in cold regions reduces power generation by degrading aerodynamic efficiency and causes mass imbalance and fatigue loads on the blades. Due to blade rotation and variation of the pitch angle, different locations on the blade experience large variation of Reynolds number, Nusselt number, heat loss, and nonuniform ice distribution. Hence, applying different amounts of heat flux in different blade locations can provide more effective de-icing for the same total power consumption. This large variation of required heat flux highly motivates using distributed resistive heating with the capability of locally adjusting thermal power as a function of location on the blade. Under medium/severe icing conditions, active de-icing with accurate direct ice detection is more energy efficient and effective in keeping the blade ice-free. This chapter includes: (1) A literature study on different methods of ice detection and a review on passive and active anti/de-icing techniques on wind turbines, (2) Development of an optical ice sensing method for direct detection of ice on the blade, including experimental results, (3) Development of an aero/thermodynamic model, which predicts how much heat flux is needed locally for de-icing under variable atmospheric conditions, (4) Experimental results showing a proof of concept of closed-loop de-icing using distributed optical ice sensing and resistive heating, and (5) Numerical modeling of ice melting on a blade for different distributed heater layouts and geometries in order to optimize thermal actuation strategy, improve de-icing efficiency, and reduce power consumption. We conclude with discussions of future directions on distributed ice sensing and thermal actuation for the next generation of de-icing systems on wind turbines.


Active de-icing Distributed actuation Localized heating Optical ice sensing Optical frequency domain reflectometry 



Angle of attack


Chord length of the blade

Transform-limited time resolution of an optical measurement


Error signal j between desired blade temperature and actual blade temperature


Natural convective heat transfer coefficient of air


Performance cost function


Derivative gain of a PID controller


Gain of the op-amp circuit


Integral gain of a PID controller


Proportional gain of a PID controller


Thermal conductivity


Thermal conductivity of air


Local Nusselt number on the airfoil


Total number of thermal resistors in the network


Rated power produced by the wind turbine


Total average power consumption for the distributed thermal resistor network


Input heat flux to the thermal resistor


Convective loss heat flux


Maximum resistor heat flux at maximum applied voltage


Resistance of heater element i


Span-wise radius of the blade tip


Span-wise distance from the blade hub

\(T_{{a_{i} }} (t)\)

Current temperature on the blade for channel i at time t


Ambient temperature


Maximum desired blade temperature

\(T_{{{ \hbox{max} }_{b} }}\)

Maximum global temperature applied to the blade structure during de-icing


Time after switching on the resistor network


De-icing time


Wind speed


Volume of ice residue


Volume of the blade experiencing temperature higher than T d


Input voltage to the resistors


Applied voltage to resistor i


DC input voltage to the resistor at maximum power


Weighting matrix in de-icing performance cost function


De-icing performance state vector


Chord-wise distance from the leading edge of the blade


Chord−wise blade axis along resistor columns


Span−wise blade axis along resistor rows


Angular velocity of the rotating blade



The authors would like to thank Patrick Wagner from the University of Colorado Boulder for his help in the fabrication of the experimental setup, Dr. Eric D. Moore from Chiaro Technologies LLC for his help in integrating the closed-loop control module and the signal processing codes for ice detection into a pre−developed OFDR software module, Dr. Patrick Moriarty from the U.S. National Renewable Energy Laboratory (NREL) for providing a blade part for our experiments, Prof. Kurt Maute from the University of Colorado Boulder for suggesting the ANSYS software for the numerical modeling of ice melting, and Dr. Ali Najafi from ANSYS Inc. for explaining how to calculate the volume of ice residue in ANSYS. The authors would also like to thank Fiona Dunne, Eric J. Simley, Jacob Aho, Jason Laks, Andrew Buckspan, and Hua Zhong for their valuable comments and feedback during the progress of this research at the University of Colorado Boulder.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shervin Shajiee
    • 1
  • Lucy Y. Pao
    • 2
    Email author
  • Robert R. McLeod
    • 2
  1. 1.Department of Aerospace Engineering SciencesUniversity of ColoradoBoulderUSA
  2. 2.Department of Electrical, Computer, and Energy EngineeringUniversity of ColoradoBoulderUSA

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