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

19 Citations (Scopus)


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
Issue number1
Publication statusPublished - 2005



  • Electromagnetic fields
  • Neural nets
  • Pipelines

ASJC Scopus subject areas

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

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