Artificial Neural Network based Method for Fault Location in Distribution Systems

Coser, J.; Vale, Douglas Terêncio do; Rolim, J.G.

doi:10.1109/isap.2005.1599255

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…Modular Neural Network algorithm, on the other hand, able to resolve the complexity issue effortlessly which makes it the optimal selection for the distribution fault location application. The article in [27] uses Modular Neural Network algorithm to locate fault incident on the distribution system. Nonhomogeneous features of the radial distribution feeder and lateral sources are considered in the application of the algorithm Neural network algorithm structure can be divided into Multilayer Perceptron (MLP) and Radial Basis Function Network (RBFN) [28].…”

Section: Intelligence Based Methodsmentioning

confidence: 99%

A Comprehensive Review of Fault Location Methods for Distribution Power System

Gana

Aziz

Ali

et al. 2017

IJEECS

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Section: Intelligence Based Methodsmentioning

confidence: 99%

A Comprehensive Review of Fault Location Methods for Distribution Power System

Gana

Aziz

Ali

et al. 2017

IJEECS

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An advanced wide-area fault detection and location technique for transmission lines considering optimal phasor measurement units allocation

el‐Ghany

Soliman

ELGebaly

2022

Alexandria Engineering Journal

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Malaysia solar energy experience: Intelligent fault location algorithm for unbalanced radial distribution network including PV systems

Farzan

Izadi

Gomes

et al. 2016

Journal of Renewable and Sustainable Energy

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Due to environmental issues and the upward trend of fossil fuel prices, the study of renewable energy (RE) based generation and their effects on the electrical system has become an important part of the government's energy policies and university projects. In RE generation, as solar photovoltaic (PV) systems are modular, silent, and transportable and demonstrate ease of installation, they have attracted a greater amount of attention specifically in those areas which receive considerable average solar radiation per day such as Malaysia. However, connecting solar PV farms to the grid like any other distributed generation (DG) units poses serious issues which arise in the distribution network. This paper presents a novel fault location algorithm based on the recording of short circuit power values at the primary substation of unbalanced radial distribution networks including PV systems. The recorded values are evaluated by a designed and tuned multi-layer feed forward neural network and the fault distances from the source are estimated accordingly. In order to highlight the accuracy of the presented method, the scenario is also repeated by recording the peak values of short circuit current which have been mostly used in the published intelligent fault location studies and the obtained results via two different values are compared with each other. The results reveal that the presented algorithm using a small scale input set is able to precisely locate different fault types in the unbalanced distribution networks including DG units.

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Artificial Neural Network based Method for Fault Location in Distribution Systems

Cited by 6 publications

References 8 publications

A Comprehensive Review of Fault Location Methods for Distribution Power System

A Comprehensive Review of Fault Location Methods for Distribution Power System

An advanced wide-area fault detection and location technique for transmission lines considering optimal phasor measurement units allocation

Malaysia solar energy experience: Intelligent fault location algorithm for unbalanced radial distribution network including PV systems

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