Neuro-Fuzzy Based Fault Detection Identification and Location in a Distribution Network

被引：0

作者：

Babayomi, Oluleke ^{[1
]}

Oluseyi, Peter ^{[2
]}

Keku, Godbless ^{[3
]}

Ofodile, Nkemdilim A. ^{[4
]}

机构：

[1] Nat Space Res & Dev Agcy, Cent Space Transport & Prop, Abuja, Nigeria

[2] Univ Lagos, Dept Elect & Elect Engn, Lagos, Nigeria

[3] Nig Mar Admin & Safety Agcy, Plan Res & Data Mgt Serv, Apapa, Nigeria

[4] Nigerian Air Force, Nig Air Force Res & Dev Cent, Abuja, Nigeria

来源：

2017 IEEE PES POWERAFRICA CONFERENCE | 2017年

关键词：

three phase faults; adaptive neuro-fuzzy inference system; distribution network; fault identification; fault location; fuzzy-logic; POWER-SYSTEMS; DIAGNOSIS;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper presents an investigation into neuro-fuzzy techniques for the accurate detection, classification and location of an electric power fault in a distribution network. Ten different types of faults were studied with respect to a real network. These include: line-to-ground faults (on each of phases A, B and C); line-to-line faults (on phases A-B, B-C and A-C); line-to-line-to-ground faults (on phases A-B, B-C and A-C) and three phase fault. A Mandami-type fuzzy controller was also applied to fault type determination. The results reveal that the developed models detect, identify and locate fault incidences to a high degree of accuracy.

引用

页码：164 / 168

页数：5

共 50 条

[1] Neuro-fuzzy identification applied to fault detection in nonlinear systems
Felipe Blazquez, L.
de Miguel, Luis J.
Aller, Fernando
Peran, Jose R.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2011, 42 (10) : 1771 - 1787
[2] Wavelet and neuro-fuzzy based fault location for combined transmission systems
Jung, C. K.
Kim, K. H.
Lee, J. B.
Kloeckl, Bernd
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2007, 29 (06) : 445 - 454
[3] Network Intrusion Detection Based on Neuro-Fuzzy Classification
Toosi, Adel Nadjaran
Kahani, Mohsen
Monsefi, Reza
2006 INTERNATIONAL CONFERENCE ON COMPUTING & INFORMATICS (ICOCI 2006), 2006, : 345 - +
[4] On a Fault Detection System based on Neuro-Fuzzy Fusion Method
Xu, Ye
Wang, Zhuo
2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, : 3190 - 3193
[5] Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)
Mehdi Gholizadeh
Alireza Yazdizadeh
Hamed Mohammad-Bagherpour
Pattern Analysis and Applications, 2019, 22 : 359 - 373
[6] Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)
Gholizadeh, Mehdi
Yazdizadeh, Alireza
Mohammad-Bagherpour, Hamed
PATTERN ANALYSIS AND APPLICATIONS, 2019, 22 (02) : 359 - 373
[7] Application of a neuro-fuzzy network in multi-sensor fault detection and diagnosis
Shen, Y.
Liu, Y.P.
Liu, Z.Y.
Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2001, 12 (10):
[8] Neuro-Fuzzy fault detection method for photovoltaic systems
Bonsignore, Luca
Davarifar, Mehrdad
Rabhi, Abdelhamid
Tina, Giuseppe M.
Elhajjaji, Ahmed
6TH INTERNATIONAL CONFERENCE ON SUSTAINABILITY IN ENERGY AND BUILDINGS, 2014, 62 : 431 - 441
[9] Identification of Tomato Leaf Diseases based on a Deep Neuro-fuzzy Network
Tian X.
Meng X.
Wu Q.
Chen Y.
Pan J.
Journal of The Institution of Engineers (India): Series A, 2022, 103 (02) : 695 - 706
[10] TRANSIENT FAULT AREA LOCATION AND FAULT CLASSIFICATION FOR DISTRIBUTION SYSTEMS BASED ON WAVELET TRANSFORM AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)
Khaleghi, Ali
Oukati Sadegh, Mahmoud
Ghazizadeh-Ahsaee, Mahdi
Mehdipour Rabori, Alireza
ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING, 2018, 16 (02) : 155 - 166

← 1 2 3 4 5 →