Deterioration of Epoxy Mold Insulations Due to Voltage Endurance

Honda, Masaki; Aoyagi, H.; Koya, M.; Kobayashi, N.; Menju, S.

doi:10.1016/b978-0-08-029381-3.50045-9

Cited by 1 publication

(1 citation statement)

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“…It will further threaten the safety of GIS equipment. Although SF 6 is non-toxic, some SF 6 decomposition products are toxic such as S 2 F 10 , SF 4 , SOF 2 , SO 2 F 2 , SOF 4 , and HF, which will threaten the GIS equipment and environmental protection 3 , 4 . Therefore, an effective pre-diagnosis method for discharge faults is necessary for the current industrial GIS system to recognize and maintain insulation defects.…”

Section: Introductionmentioning

confidence: 99%

Effects of SF6 decomposition components and concentrations on the discharge faults and insulation defects in GIS equipment

Zhuang

Tang³

et al. 2020

Sci Rep

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Gas-insulated switchgear (GIS) is widely used across multiple electric stages and different power grid levels. However, the threat from several inevitable faults in the GIS system surrounds us for the safety of electricity use. In order to improve the evaluation ability of GIS system safety, we propose an efficient strategy by using machine learning to conduct SF6 decomposed components analysis (DCA) for further diagnosing discharge fault types in GIS. Note that the empirical probability function of different faults fitted by the Arrhenius chemical reaction model has been investigated into the robust feature engineering for machine learning based GIS diagnosing model. Six machine learning algorithms were used to establish models for the severity of discharge fault and main insulation defects, where identification algorithms were trained by learning the collection dataset composing the concentration of the different gas types (SO2, SOF2, SO2F2, CF4, and CO2, etc.) in the system and their ratios. Notably, multiple discharge fault types coexisting in GIS can be effectively identified based on a probability model. This work would provide a great insight into the development of evaluation and optimization on solving discharge fault in GIS.

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Section: Introductionmentioning

confidence: 99%