Fault Location in Distribution Network Based on Phasor Measurement Units (PMU)

Dashtdar, Majid

doi:10.1515/sbeef-2019-0019

Cited by 9 publications

(2 citation statements)

References 26 publications

(24 reference statements)

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“…Therefore, in order to improve network dependability, faulty areas must be diagnosed quickly and accurately. With the advancement of technology, PMUs can be utilized in electricity networks to enable automatic fault location [4]. A micro-PMU is a type of PMU that is designed to record the quantities of the medium voltage side of a distribution network [5].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Mirshekali

Dashti

Keshavarz

et al. 2022

Sensors

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Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Mirshekali

Dashti

Keshavarz

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…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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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 Distribution Network Based on Phasor Measurement Units (PMU)

Cited by 9 publications

References 26 publications

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

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

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