TY - JOUR
T1 - Prediction of metallic conductor voltage owing to electromagnetic coupling using neuro fuzzy modeling
AU - Al-Badi, A. H.
AU - Ghania, Samy M.
AU - El-Saadany, Ehab F.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Adaptive neuro-fuzzy
KW - Electromagnetic
KW - Interference
KW - Pipelines
KW - Power transmission lines
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U2 - 10.1109/TPWRD.2008.2002657
DO - 10.1109/TPWRD.2008.2002657
M3 - Article
AN - SCOPUS:58249142105
SN - 0885-8977
VL - 24
SP - 319
EP - 327
JO - IEEE Transactions on Power Delivery
JF - IEEE Transactions on Power Delivery
IS - 1
ER -