Determining the Fault Position in the Power System

Šipoš, Mario; Klaić, Zvonimir; Nyarko, Emmanuel Karlo; Fekete, Krešimir; Primorac, Mario; Šljivac, Damir

doi:10.1109/sst49455.2020.9264078

Cited by 1 publication

(1 citation statement)

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“…The research results in [14], the optimal number and placement of voltage dip monitors, gave us an idea and possibility for the next step: determining the location of the fault based on recorded voltage dips [15]. In [15], fault-affected lines were classified using the decision tree algorithm, giving accurate results. However, using multiple linear regression to determine the fault location at the line length did not give satisfactory results for all lines.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A method for determining the location and type of fault in transmission network using neural networks and power quality monitors

Šipoš,

KLAIĆ,

Nyarko

et al. 2024

Eksploatacja i Niezawodność – Maintenance and Reliability

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A new technique for identifying the location of a fault on a power line utilizing neural networks is presented in this paper. Specifically, the procedure involves four stages (three of which employ neural networks): gathering voltage input data via simulation, classifying the fault type, detecting the faulted line, and determining the fault position on the power line. This model was developed and tested for the IEEE 39 bus test system. Input voltages are obtained using DigSILENT PowerFactory software in which a set of three-phase and single-phase short circuits are simulated. Not voltages from all buses are used for the subsequent stages, only voltages from the optimally placed 12 buses in the IEEE 39 bus test system are used. In the second step, the first neural network is employed in order to classify the fault type – single-phase or three-phase. In the second stage, another neural network is used to determine the faulted line and in the third stage, the last neural network is developed to determine the fault position on the faulted line.

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Section: Theoretical Backgroundmentioning

confidence: 99%