An artificial neural network model for predicting gas pipeline induced voltage caused by power lines under fault conditions

S. Al-Alawi, A. Al-Badi, K. Ellithy

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Purpose - Presents a technique based on the development of an artificial neural network (ANN) model for predicting the electromagnetic inference effects on gas pipelines shared right-of-way (ROW) with high voltage transmission lines. Design/methodology/approach - Examines the induced pipeline voltage under different soil resistivity, fault current and separation distance. Findings - The results indicate strong agreement between model prediction and observed values. Originality/value - Demonstrates that the ANN-based model developed can predict the induced voltage with high accuracy. The accuracy of the predicted induced voltage is very important for designing mitigation systems that will increase overall pipeline integrity and make the pipeline and appurtenances safe for operating personnel.

Original languageEnglish
Pages (from-to)69-80
Number of pages12
JournalCOMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
Volume24
Issue number1
DOIs
Publication statusPublished - 2005

Fingerprint

Gas pipelines
Neural Network Model
Artificial Neural Network
Fault
Voltage
Neural networks
Pipelines
Line
Electric potential
Rights of way
Electric fault currents
Resistivity
Transmission Line
Prediction Model
Integrity
Design Methodology
Soil
Electric lines
High Accuracy
Personnel

Keywords

  • Electromagnetic fields
  • Neural nets
  • Pipelines

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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T1 - An artificial neural network model for predicting gas pipeline induced voltage caused by power lines under fault conditions

AU - Al-Alawi, S.

AU - Al-Badi, A.

AU - Ellithy, K.

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N2 - Purpose - Presents a technique based on the development of an artificial neural network (ANN) model for predicting the electromagnetic inference effects on gas pipelines shared right-of-way (ROW) with high voltage transmission lines. Design/methodology/approach - Examines the induced pipeline voltage under different soil resistivity, fault current and separation distance. Findings - The results indicate strong agreement between model prediction and observed values. Originality/value - Demonstrates that the ANN-based model developed can predict the induced voltage with high accuracy. The accuracy of the predicted induced voltage is very important for designing mitigation systems that will increase overall pipeline integrity and make the pipeline and appurtenances safe for operating personnel.

AB - Purpose - Presents a technique based on the development of an artificial neural network (ANN) model for predicting the electromagnetic inference effects on gas pipelines shared right-of-way (ROW) with high voltage transmission lines. Design/methodology/approach - Examines the induced pipeline voltage under different soil resistivity, fault current and separation distance. Findings - The results indicate strong agreement between model prediction and observed values. Originality/value - Demonstrates that the ANN-based model developed can predict the induced voltage with high accuracy. The accuracy of the predicted induced voltage is very important for designing mitigation systems that will increase overall pipeline integrity and make the pipeline and appurtenances safe for operating personnel.

KW - Electromagnetic fields

KW - Neural nets

KW - Pipelines

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