Open AccessApplication technique for model‐based approach to estimate fault location
Author(s) -
Navalpakkam Ananthan Sundaravaradan,
Santoso Surya
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0135
Subject(s) - fault (geology) , fault indicator , electric power transmission , fault model , relay , fault coverage , line (geometry) , computer science , stuck at fault , transmission line , electric power system , artificial neural network , power (physics) , real time computing , reliability engineering , engineering , fault detection and isolation , artificial intelligence , electronic circuit , electrical engineering , telecommunications , seismology , geology , physics , geometry , mathematics , quantum mechanics , actuator
Impedance‐based algorithms commonly used for determining the fault location in transmission lines are prone to several sources of error and are specific to the line and system configuration. Furthermore, these algorithms do not utilise available valuable information about the power system surrounding the faulted line. These issues can be overcome using a model‐based fault location (MBFL) approach. It uses a circuit model to simulate possible fault scenarios and compares the simulated fault currents with the measured currents recorded by the relay to identify the fault location. However, there are several difficulties and limitations while applying MBFL. There is a loss in accuracy and precision based on the number of simulated scenarios and a requirement to store voluminous simulation results. Hence, this study presents a novel application technique for implementing model‐based approach efficiently to estimate the fault location and fault resistance using artificial neural networks‐based approach. A key highlight of the proposed approach is the ability to identify the location of a fault present on neighbouring lines using the measured through fault current. The study also presents representative scenarios to demonstrate the capability and potential of the proposed approach.