Application technique for model-based approach to estimate fault location

• Published in: IET Smart Grid Vol. 3; no. 4; pp. 421 - 434
• Main Authors: Navalpakkam Ananthan, Sundaravaradan, Santoso, Surya
• Format: Journal Article
• Language: English
• Published: Durham The Institution of Engineering and Technology 01.08.2020; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Artificial neural networks; B8120 Power transmission, distribution and supply; B8130 Power transmission lines and cables; B8140 Power system protection; C5290 Neural computing techniques; C7410B Power engineering computing; circuit model; Engineers; fault current; fault currents; Fault location; fault location estimation; fault resistance; faulted line; impedance-based algorithms; model-based fault location approach; neural nets; Neural networks; power engineering computing; power system; power transmission faults; power transmission lines; simulated fault currents; Software; Special Issue: Machine Learning in Power Systems; system configuration; Transmission lines; power transmission lines; fault current; faulted line; fault resistance; power system; fault location; system configuration; fault currents; impedance-based algorithms; simulated fault currents; model-based fault location approach; power engineering computing; transmission lines; fault location estimation; artificial neural networks; power transmission faults; circuit model; neural nets
• ISSN: 2515-2947; 2515-2947
• DOI: 10.1049/iet-stg.2019.0135

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.