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Dielectric Strength of Liquid Helium under Strongly Inhomogeneous Field Conditions

  • M. M. Menon
  • S. W. Schwenterly
  • W. F. Gauster
  • R. H. Kernohan
  • H. M. Long
Part of the Advances in Cryogenic Engineering book series (ACRE, volume 21)

Abstract

In recent years a great deal of attention has been directed to the application of superconductivity to electrical equipment, in particular, superconducting power transmission lines [1]. Liquid helium is being investigated in a dual role as the dielectric and coolant in some cable projects presently under way [2–4], in view of its extremely low dielectric loss (< μ rad). Most of the information available on the breakdown strength of liquid helium is limited to either uniform or quasiuniform field conditions. Nevertheless, in any practical system one often encounters bends, corners, and projections. Frozen contaminants on the electrode surface, for instance, water vapor, could provide serious nonuniform field configurations. It is therefore necessary to evaluate the performance of the dielectric under strongly inhomogeneous field conditions. In order to do this the ASTM standard electrode and the point-plane electrode systems were employed. Although the main emphasis was directed toward measurements on liquid helium, in certain cases liquid nitrogen and cold helium gas were also studied. It was found that the deterioration in dielectric strength of liquid helium in strongly inhomogeneous field conditions is much more severe than that of well-degassed transformer oil. Also, in the case of the point-plane electrode configuration, liquid helium exhibits a strong polarity effect markedly different from that of transformer oil. The breakdown data were also subjected to statistical analysis in order to determine the form of the distribution function.

Keywords

Liquid Helium Breakdown Strength Dielectric Strength Negative Point Point Electrode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1960

Authors and Affiliations

  • M. M. Menon
    • 1
  • S. W. Schwenterly
    • 1
  • W. F. Gauster
    • 1
  • R. H. Kernohan
    • 1
  • H. M. Long
    • 1
  1. 1.Oak Ridge National LaboratoryOak RidgeUSA

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