Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions

Sajjad, Umer and Arshad, A and Ahmad, Jawad and Shoaib, Sultan; Shaposhnikov, S., ed. (2021) Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions. In: 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, SUN, pp. 2868-2873. ISBN 9781665404761 (https://doi.org/10.1109/ElConRus51938.2021.9396388)

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Abstract

Safe and reliable delivery of power through transmission lines mainly depends on the quality condition of the high voltage insulators. In the last few decades, demand in polymeric insulator has been dramatically increased due to their advanced performance in comparison to ceramic and glass insulators. This paper discusses the application of Artificial Neural Network (ANN) to predict the flashover parameters of polymeric insulators under the impact of weather and environment conditions. The training data for ANN were obtained from experimental tests executed in the climate chamber with the implementation of high voltage stress. The parameters predicted in this paper are arc-inception voltage, flashover voltage and surface resistance. A promising application of the ANN model proposed in this paper is the effective prediction of the flashover parameters of polymeric insulators affecting by extreme temperature, humidity and pollution level. These results will also enhance our understanding of the flashover process in outdoor polymeric insulators.

ORCID iDs

Sajjad, Umer, Arshad, A ORCID logoORCID: https://orcid.org/0000-0001-8621-2773, Ahmad, Jawad and Shoaib, Sultan; Shaposhnikov, S.
  • Item type:Book Section
    ID code:76858
    Dates:
    Date
    Event
    9 April 2021
    Published
    5 December 2020
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:22 Jun 2021 11:14
    Last modified:11 Nov 2024 15:25
    URI:https://strathprints.strath.ac.uk/id/eprint/76858
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