Detecting the Fault Section in the Distribution Network with Distributed Generators Based on Optimal Placement of Smart Meters

Dashtdar, Majid

doi:10.1515/sbeef-2019-0017

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…If enough information is available, an accurate answer can be obtained in a short time away from human error. In [16][17][18][19][20][21][22][23][24][25][26], smart meter devices and artificial neural network techniques are used to detect the faulted section and fault location in the distribution network.…”

Section: Introductionmentioning

confidence: 99%

Fault Location in Radial Distribution Network Based on Fault Current Profile and the Artificial Neural Network

Dashtdar

2020

The Scientific Bulletin of Electrical Engineering Faculty

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Electricity distribution systems are subject to a variety of faults such as permanent and transient short circuits due to the extent and multiplicity of equipment. In principle, short circuit fault causes the existing protective equipment to operate and to no electricity the various parts of the distribution network. Rapid and accurate determination of fault location, repair and recovery, it has not prevented the distribution of energy. This will satisfy consumers and prevent the losses of electricity companies. In this paper, the artificial neural network and fault current profiles are used to determine the distance of the fault, determine the type of fault and detect the short circuit. This method provides the information needed to locate the fault by sampling the current before and after the fault occurs from the SCADA system. The effect of connectivity local resistance changes and the effect of load changes on fault location were evaluated. The results show that this method is more accurate than the voltage droop profile variation method in determining the fault distance and short circuit breakdown. If only the net fault current changes profile is used, the effect of the load changes in determining the short-circuit breakdown is much less.

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Section: Introductionmentioning

confidence: 99%

Fault Location in Radial Distribution Network Based on Fault Current Profile and the Artificial Neural Network

Dashtdar

2020

The Scientific Bulletin of Electrical Engineering Faculty

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Fault location in distribution network by solving the optimization problem using genetic algorithm based on the calculating voltage changes

et al. 2022

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Fault location in the distribution network based on power system status estimation with smart meters data

Dashtdar

Hosseinimoghadam

2021

International Journal of Emerging Electric Power Systems

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Due to the existence of different branches in the electricity distribution network and only available voltage and current information at the beginning of the line and lack of access to the information at the end of the network line, the detection of the faulted section in the distribution network has become more important. Today, smart meters are used to measure the voltage and current of network lines, but due to the limitations of the installation sites, these devices are not possible in all network lines. In this paper, two techniques have been used to identify the faulted section and fault location in the network so that the fault distance at the beginning of the line can be estimated by estimating the current at the end of each network line. Therefore, in this project, by installing smart meters in the main branch of the network and also the information obtained from power flow in the network normal mode, it has been tried to practically estimate the voltage and current at the beginning and end of each distribution network line. In this method, more power flow is used to calculate the voltage drop of the lines and estimate the voltage and current at the end of the network lines so that the faulted part can be identified. Finally, the proposed method is implemented on the IEEE_15, 37 bus networks, the results of which show the proper performance of the proposed method in estimating location and Fault resistance for different types of faults in the distribution network.

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Detecting the Fault Section in the Distribution Network with Distributed Generators Based on Optimal Placement of Smart Meters

Cited by 4 publications

References 20 publications

Fault Location in Radial Distribution Network Based on Fault Current Profile and the Artificial Neural Network

Fault Location in Radial Distribution Network Based on Fault Current Profile and the Artificial Neural Network

Fault location in distribution network by solving the optimization problem using genetic algorithm based on the calculating voltage changes

Fault location in the distribution network based on power system status estimation with smart meters data

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