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Capacitive probe for ice detection and accretion rate measurement: Proof of concept

Author

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  • Owusu, Kwadwo Poku
  • Kuhn, David C.S.
  • Bibeau, Eric L.

Abstract

We demonstrate a concept potentially suitable for detecting in-cloud ice and indication of ice accretion rate within a laboratory setting. The concept measures the change in capacitance and resistance due to ice accretion between two charged cylindrical probes. An ice sensor using this concept could be located on meteorological towers or nacelle of wind turbines for icing measurements. During the controlled icing experiments, ice builds up on the cylindrical probes: capacitance increases and resistance decreases. Capacitance correlates with the mass, thickness and the location of ice deposits; the type of icing—glaze versus rime—correlates well with resistance measurements. A 2-D electric field simulation around cylindrical probes demonstrates the sensitivity of this ice sensor as a function of the distance between the probes and the location of ice deposition around the probes. Capacitance measurements using different acrylic annuli machined to high tolerances and placed over each probe to simulate ice validates the electric field simulation. Experimental tests of the cylindrical probes in an icing wind tunnel facility under controlled rime and glaze ice conditions validate the concept.

Suggested Citation

  • Owusu, Kwadwo Poku & Kuhn, David C.S. & Bibeau, Eric L., 2013. "Capacitive probe for ice detection and accretion rate measurement: Proof of concept," Renewable Energy, Elsevier, vol. 50(C), pages 196-205.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:196-205
    DOI: 10.1016/j.renene.2012.06.003
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    Cited by:

    1. Guo, Peng & Infield, David, 2021. "Wind turbine blade icing detection with multi-model collaborative monitoring method," Renewable Energy, Elsevier, vol. 179(C), pages 1098-1105.
    2. Zhijin Zhang & Hang Zhang & Xu Zhang & Qin Hu & Xingliang Jiang, 2024. "A Review of Wind Turbine Icing and Anti/De-Icing Technologies," Energies, MDPI, vol. 17(12), pages 1-34, June.
    3. Cheng Tao & Tao Tao & Xinjian Bai & Yongqian Liu, 2023. "Wind Turbine Blade Icing Prediction Using Focal Loss Function and CNN-Attention-GRU Algorithm," Energies, MDPI, vol. 16(15), pages 1-15, July.

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