Open AccessVoltage–current technique to identify fault location within long transmission lines
Author(s) -
AbuSiada Ahmed,
Mosaad Mohamed I.,
Mir Saif
Publication year - 2020
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2020.1012
Subject(s) - robustness (evolution) , electric power transmission , fault (geology) , computer science , fault indicator , transmission line , electric power system , reliability engineering , voltage , fault detection and isolation , electrical impedance , real time computing , line (geometry) , electronic engineering , engineering , power (physics) , electrical engineering , telecommunications , seismology , geology , biochemistry , chemistry , physics , geometry , mathematics , quantum mechanics , actuator , gene
Current industry practice to identify fault location in transmission lines is based on visual inspection, travelling waves, and line impedance measurement. Unfortunately, these techniques are only developed to detect the fault location upon its occurrence without the ability to predict abnormal events that usually precede major faults and issue a timely warning signal to avoid potential consequences for power line failures. Furthermore, the current fault locating techniques exhibit some drawbacks that limit their wide practical implementation. This includes cost, access to required data, and low accuracy when employed for specific power line topologies. This study is aimed at presenting and validating a new cost‐effective technique based on the line voltage–current characteristics to predict and identify the location of various abnormal and fault events in real‐time. By measuring the currents and voltages at both ends of the line, a unique line fingerprint can be identified. Any change in this fingerprint can be detected and analysed by a software installed in the control centre in real‐time to identify the location, type, and level of the abnormal events or emerging faults. Robustness of the proposed technique is assessed through simulation analysis conducted on various case studies along with a practical case study.