Open AccessSimultaneous fault detection and diagnosis in electric power system using hybrid method
Author(s) -
Afaneen Anwar,
Rana Ali Abttan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.14146
Subject(s) - fault indicator , fault (geology) , electric power system , fault detection and isolation , fault coverage , reliability (semiconductor) , computer science , electric power transmission , stuck at fault , matlab , artificial neural network , transmission line , busbar , real time computing , power (physics) , engineering , reliability engineering , artificial intelligence , electrical engineering , telecommunications , physics , quantum mechanics , seismology , electronic circuit , actuator , geology , operating system
Simultaneous fault is one of the challenging issues. Faults are the major hurdles in power system designing and protection .Simultaneous fault is the combination of faults indicates that that two or more faults which occur at the same time.The main objective of simultaneous fault detection, classification and location is satisfy accelerates line restoration, maintains system, stability, repairs the fault, decreases the restoration time and increases the system reliability.This paper presents an approach for analysis, detection, classification and location for simultaneous faults in bus bar and transmission line. Two port network is adapted for analysis , voltage and current measurement method is adapted in the fault detection, neural network in the fault classification and location for different types of fault and places were to estimation accurately fault location by analyzing the data available after the beginning of disturbance.All programs were written in MATLAB environment. The programs were test on IEEE- 11 bus bar network. The results clarified that the voltage and current measurement method and impedance method is very effective for simultaneous fault detection, classification and location.