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State-of-the-art on power line de-icing

Open access
Laforte, J.L., Allaire, M.A., Laflamme, J.·1998·Atmospheric Research

Abstract

This paper presents a detailed review of the different de-icing techniques, already developed and in development, which could be applied to the conductors and wires of electric power lines. After a bibliographical search in various data banks, on de-icing processes, more than 30 techniques at different stages of development, capable of removing ice and assuring anti-icing protection, have been identified in different fields such as air and rail transport, electrical networks, telecommunications, etc. Although many techniques have not yet made it beyond the concept stage, some are used in several of the sectors previously mentioned. The following is a comparative evaluation of all these techniques, thermal, mechanical and passive, based on energy efficiency and practicability. It is recommended to favor the mechanical techniques over thermal methods that have been developed, but require more energy. Specific development projects and feasibility studies on the most attractive techniques have finally been identified as steps for progress in power line de-icing.

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Metadata

Authors
Laforte, J. L., Allaire, M. A., & Laflamme, J.
Venue
Atmospheric Research
Publisher
Elsevier BV
Published
1998-04
Volume
46
Issue
1-2
Pages
143-158
ISSN
0169-8095 (ISSN-L)
OpenAlex
W1983189968
Cited by
475
References
0
FWCI
0.00
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: State-of-the-art on power line de-icing. https://app.cochat.ai/papers/10.1016/s0169-8095(97)00057-4
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Laforte, J. L., Allaire, M. A., & Laflamme, J. (1998). State-of-the-art on power line de-icing. Atmospheric Research. https://doi.org/10.1016/s0169-8095(97)00057-4

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.