Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

Patrick S.Pouabe Eboule; Jan Harm C. Pretorius; Nhlanhla Mbuli; Collins Leke

doi:10.1109/EPEC.2018.8598311

Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

Patrick S.Pouabe Eboule, Jan Harm C. Pretorius, Nhlanhla Mbuli, Collins Leke

Postgraduate School of Engineering Management

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Citations (Scopus)

Abstract

In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.

Original language	English
Title of host publication	2018 IEEE Electrical Power and Energy Conference, EPEC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538654194
DOIs	https://doi.org/10.1109/EPEC.2018.8598311
Publication status	Published - 31 Dec 2018
Event	2018 IEEE Electrical Power and Energy Conference, EPEC 2018 - Toronto, Canada Duration: 10 Oct 2018 → 11 Oct 2018

Publication series

Name	2018 IEEE Electrical Power and Energy Conference, EPEC 2018

Conference

Conference	2018 IEEE Electrical Power and Energy Conference, EPEC 2018
Country/Territory	Canada
City	Toronto
Period	10/10/18 → 11/10/18

Keywords

Artificial Neural Network
Concurrent Neuro Fuzzy
Faults Classification
Faults Location
Fuzzy-Logic
Power Systems
Power Transmission Lines
faults detection

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Control and Optimization

Access to Document

10.1109/EPEC.2018.8598311

Cite this

Eboule, P. S. P., Pretorius, J. H. C., Mbuli, N., & Leke, C. (2018). Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique. In 2018 IEEE Electrical Power and Energy Conference, EPEC 2018 Article 8598311 (2018 IEEE Electrical Power and Energy Conference, EPEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEC.2018.8598311

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title = "Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique",

abstract = "In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.",

keywords = "Artificial Neural Network, Concurrent Neuro Fuzzy, Faults Classification, Faults Location, Fuzzy-Logic, Power Systems, Power Transmission Lines, faults detection",

author = "Eboule, {Patrick S.Pouabe} and Pretorius, {Jan Harm C.} and Nhlanhla Mbuli and Collins Leke",

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day = "31",

doi = "10.1109/EPEC.2018.8598311",

language = "English",

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Eboule, PSP, Pretorius, JHC, Mbuli, N & Leke, C 2018, Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique. in 2018 IEEE Electrical Power and Energy Conference, EPEC 2018., 8598311, 2018 IEEE Electrical Power and Energy Conference, EPEC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Electrical Power and Energy Conference, EPEC 2018, Toronto, Canada, 10/10/18. https://doi.org/10.1109/EPEC.2018.8598311

Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique. / Eboule, Patrick S.Pouabe; Pretorius, Jan Harm C.; Mbuli, Nhlanhla et al.
2018 IEEE Electrical Power and Energy Conference, EPEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8598311 (2018 IEEE Electrical Power and Energy Conference, EPEC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

AU - Eboule, Patrick S.Pouabe

AU - Pretorius, Jan Harm C.

AU - Mbuli, Nhlanhla

AU - Leke, Collins

PY - 2018/12/31

Y1 - 2018/12/31

N2 - In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.

AB - In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.

KW - Artificial Neural Network

KW - Concurrent Neuro Fuzzy

KW - Faults Classification

KW - Faults Location

KW - Fuzzy-Logic

KW - Power Systems

KW - Power Transmission Lines

KW - faults detection

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 10 October 2018 through 11 October 2018

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Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

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Keywords

ASJC Scopus subject areas

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