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

Umer Sajjad; A Arshad; Jawad Ahmad; Sultan Shoaib

doi:10.1109/ElConRus51938.2021.9396388

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

Umer Sajjad, A Arshad, Jawad Ahmad, Sultan Shoaib

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

8 Downloads (Pure)

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.

Original language	English
Title of host publication	2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)
Editors	S. Shaposhnikov
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Pages	2868-2873
Number of pages	6
ISBN (Print)	9781665404761
DOIs	https://doi.org/10.1109/ElConRus51938.2021.9396388
Publication status	Published - 9 Apr 2021
Event	2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering - St Petersburg and Moscow, Russian Federation Duration: 26 Jan 2021 → 28 Jan 2021

Conference

Conference	2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering
Abbreviated title	EIConRus
Country/Territory	Russian Federation
City	St Petersburg and Moscow
Period	26/01/21 → 28/01/21

Keywords

surface resistance
training data
artificial neural networks
flashover
polymers

Access to Document

10.1109/ElConRus51938.2021.9396388

Saijad-etal-EIConRus-2021-Application-of-artificial-neural-network-in-predicting-flashover-behaviourAccepted author manuscript, 892 KB

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

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title = "Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions",

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.",

keywords = "surface resistance, training data, artificial neural networks, flashover, polymers",

author = "Umer Sajjad and A Arshad and Jawad Ahmad and Sultan Shoaib",

note = "{\textcopyright} 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.; 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus ; Conference date: 26-01-2021 Through 28-01-2021",

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doi = "10.1109/ElConRus51938.2021.9396388",

language = "English",

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Sajjad, U, Arshad, A, Ahmad, J & Shoaib, S 2021, Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions. in S Shaposhnikov (ed.), 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, Piscataway, N.J., pp. 2868-2873, 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, St Petersburg and Moscow, Russian Federation, 26/01/21. https://doi.org/10.1109/ElConRus51938.2021.9396388

Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions. / Sajjad, Umer; Arshad, A; Ahmad, Jawad et al.
2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). ed. / S. Shaposhnikov. Piscataway, N.J.: IEEE, 2021. p. 2868-2873.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions

AU - Sajjad, Umer

AU - Arshad, A

AU - Ahmad, Jawad

AU - Shoaib, Sultan

N1 - © 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.

PY - 2021/4/9

Y1 - 2021/4/9

N2 - 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.

AB - 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.

KW - surface resistance

KW - training data

KW - artificial neural networks

KW - flashover

KW - polymers

U2 - 10.1109/ElConRus51938.2021.9396388

DO - 10.1109/ElConRus51938.2021.9396388

M3 - Conference contribution book

SN - 9781665404761

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BT - 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

A2 - Shaposhnikov, S.

PB - IEEE

CY - Piscataway, N.J.

T2 - 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering

Y2 - 26 January 2021 through 28 January 2021

ER -

Sajjad U, Arshad A, Ahmad J, Shoaib S. Application of artificial neural network in predicting flashover behaviour of outdoor insulators under polluted conditions. In Shaposhnikov S, editor, 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). Piscataway, N.J.: IEEE. 2021. p. 2868-2873 doi: 10.1109/ElConRus51938.2021.9396388

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

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