Application technique for model‐based approach to estimate fault location. Issue 4 (15th April 2020)

• Record Type: Journal Article
• Authors: Navalpakkam Ananthan, Sundaravaradan; Santoso, Surya
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 421
• Page End: 434
• Publication Date: 2020-04-15
• Subjects: fault location -- fault currents -- power transmission faults -- neural nets -- power transmission lines -- power engineering computing; fault resistance -- artificial neural networks -- fault current -- impedance‐based algorithms -- system configuration -- faulted line -- model‐based fault location approach -- simulated fault currents -- fault location estimation -- transmission lines -- power system -- circuit model; B8120 Power transmission, distribution and supply -- B8130 Power transmission lines and cables -- B8140 Power system protection -- C5290 Neural computing techniques -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0135
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.