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

[31] Fault detection based on Neuro-Fuzzy models and residual evaluation with fuzzy thresholds applied to a photovoltaic system
Tojeiro, Damien Olazabal
Cabeza, Raquelita Torres
Potts, Alain Segundo
IFAC PAPERSONLINE, 2021, 54 (20): : 717 - 722
[32] A fire detection system based on ART-2 neuro-fuzzy network
Zhang, Q
Shu, W
ICSP '98: 1998 FOURTH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, PROCEEDINGS, VOLS I AND II, 1998, : 1355 - 1358
[33] Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system
Palmero, GIS
Santamaria, JJ
de la Torre, EJM
González, JRP
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2005, 18 (07) : 867 - 874
[34] Hybrid Neuro-Fuzzy Network Identification for Autonomous Underwater Vehicles
Hassanein, Osama
Sreenatha, G.
Ray, Tapabrata
SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, PT II (SEMCCO 2013), 2013, 8298 : 287 - 297
[35] Modelling of dam behaviour based on neuro-fuzzy identification
Rankovic, Vesna
Grujovic, Nenad
Divac, Dejan
Milivojevic, Nikola
Novakovic, Aleksandar
ENGINEERING STRUCTURES, 2012, 35 : 107 - 113
[36] An adaptive neuro-fuzzy identification model for the detection of meat spoilage
Kodogiannis, Vassilis S.
Alshejari, Abeer
APPLIED SOFT COMPUTING, 2014, 23 : 483 - 497
[37] Neuro-fuzzy based identification of Hammerstein OEAR systems
Lyu, Bensheng
Jia, Li
Li, Feng
COMPUTERS & CHEMICAL ENGINEERING, 2020, 141
[38] Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors
Evsukoff, A
Gentil, S
ADVANCED ENGINEERING INFORMATICS, 2005, 19 (01) : 55 - 66
[39] Electrical machine fault detection using adaptive neuro-fuzzy inference
Ye, ZM
Wu, B
Sadeghian, AR
JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5, 2001, : 390 - 394
[40] Identification, prediction and detection of the process fault in a cement rotary kiln by Locally Linear Neuro-Fuzzy technique
Sadeghian, Masoud
Fatehi, Alireza
World Academy of Science, Engineering and Technology, 2009, 34 : 1128 - 1134

← 1 2 3 4 5 →