Prediction of metallic conductor voltage owing to electromagnetic coupling using neuro fuzzy modeling

A. H. Al-Badi, Samy M. Ghania, Ehab F. El-Saadany

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Electromagnetic interference effects of transmission lines on nearby metallic structures such as pipelines, communication lines, or railroads are a real problem, which can place both operator safety and structure integrity at risk. The level of these voltages can be reduced to a safe value in accordance with the IEEE standard 80 by designing a proper mitigation system. This paper presents a Fuzzy algorithm that can predict the level of the metallic conductor voltage. The model outlined in this paper is both fast and accurate and can accurately predict the voltage magnitude even with changing system parameters (soil resistivity, fault current, separation distance, mitigated or unmitigated system). Simulation results for three different scenarios, confirm the capability of the proposed Fuzzy system model in modeling and predicting the total voltage and are found to be in good agreement with data obtained from the CDEGS software.

Original languageEnglish
Pages (from-to)319-327
Number of pages9
JournalIEEE Transactions on Power Delivery
Volume24
Issue number1
DOIs
Publication statusPublished - 2009

Fingerprint

Electromagnetic coupling
Electric potential
Electric fault currents
Railroads
Fuzzy systems
Signal interference
Mathematical operators
Electric lines
Pipelines
Soils
Communication

Keywords

  • Adaptive neuro-fuzzy
  • Electromagnetic
  • Interference
  • Pipelines
  • Power transmission lines

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Prediction of metallic conductor voltage owing to electromagnetic coupling using neuro fuzzy modeling. / Al-Badi, A. H.; Ghania, Samy M.; El-Saadany, Ehab F.

In: IEEE Transactions on Power Delivery, Vol. 24, No. 1, 2009, p. 319-327.

Research output: Contribution to journalArticle

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