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Analysis of Electrical Faults Detection Techniques: Case of 132kv Transmission Network AFAM Station
Subject area: Science,Engineering and Technology · Area of research: Power System
Abstract
Researchers compared three cutting-edge and innovative methods for identifying, classifying, and locating faults on Nigeria's 132kv transmission network. The trio consists of fuzzy logic, artificial neural networks, and an adaptive neuro-fuzzy inference system (ANFIS). To perform the comparative analysis, a MATLAB/SIMULINK model of the transmission system under consideration was built, and simulations were run for various fault types and locations. Over five different fault distances, eleven different types of faults were simulated.
Keywords
fault detection, fault Analysis, Electric Fault, Fault
References
[1] Abdullah, P., & Butler, K. (2018). Distance Protection zone 3 MisOperation During System Wide Cascading Events: The Problem and a Survey of Solutions, Journal Electric Power Systems Research, 154(1), 151–159.
[2] Cadini, G., Agliardi, L., &Zio,E.(2017). A modeling and Simulation Framework for the Reliability/Availability Assessment of a Power Transmission Grid Subject to Cascading Failures Under Extreme Weather Conditions. Journal of Applied energy, 185(2), 267-279.
[3] Idoniboyeobu, R., Braide, A., & Elsie, N. (2018).The fault Assessment and Mitigation of the 132kV Transmission Line in Nigeria.IEE J Trans Electrical Electron Eng 11(1), 43–48.
[4] Ogboh, C., &Madueme, T,(2015). Investigation of Faults on the Nigerian Power System Transmission Line Using Artificial Neural Network. Journal of Renewable and Sustainable Energy Reviews, 23(2), 342-351.
[5] Poudel, S., &Malla,M.(2017). Real-Time Cyber Physical System Test bed for Power System Security and Control. International Journal of Electrical Power & Energy Systems, 90(1), 124- 133.
[6] Staszewski, L., &Rebizant, W. (2018). DLR-Supported Over current Line Protection for Blackout Prevention, Journal of Electric Power Systems Research, 155(2), 104-110.
[7] Yang, J., & Jiang, K. (2017).The Sensitive Line Identification in Resilient Power System Based on Fault Chain Model. International Journal of Electrical Power & Energy Systems, 92(2), 212-220.
How to cite this paper
@article{1703346,
author = {AADUM, Joseph Lekie, D.C. Idoniboyeobu, C.O. Ahiakwo, S. L. Braide},
title = {Analysis of Electrical Faults Detection Techniques: Case of 132kv Transmission Network AFAM Station},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {10},
pages = {264-267},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17033461.pdf},
abstract = {Researchers compared three cutting-edge and innovative methods for identifying, classifying, and locating faults on Nigeria's 132kv transmission network. The trio consists of fuzzy logic, artificial neural networks, and an adaptive neuro-fuzzy inference system (ANFIS). To perform the comparative analysis, a MATLAB/SIMULINK model of the transmission system under consideration was built, and simulations were run for various fault types and locations. Over five different fault distances, eleven different types of faults were simulated.},
keywords = {fault detection, fault Analysis, Electric Fault, Fault},
month = {April},
}