Faults locations in automated distribution system

Gohokar, V.N.; Khedkar, Mohan

doi:10.1016/j.epsr.2005.01.003

Cited by 21 publications

(5 citation statements)

References 12 publications

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“…Ref. [9] proposed using symbolic method for detection of short circuit fault and open conductor fault in automated distribution system. This type of method needs large number of current measurements in system, it is high cost and maintenance workload.…”

Section: F Fault Indicator Based Methodsmentioning

confidence: 99%

Fault Location in Self-Healing Distribution Networks

Yin¹,

Ltd.²,

Ding³

2016

IJOEE

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Research on fault location in self-healing distribution system has been conducted for decades. However, a complete solution has not yet been found. This paper reviews the existed fault location methods in distribution system, except the SLG (Single-line-to-ground) fault in NUGS (Neutral un-effectively grounded system). The discussed faults in this paper are the short circuit faults which would cause large fault current and could be detected by over-current relay in substation. The principles and developments of several typical fault location methods are introduced in the paper. At last, possible improvements are proposed at the end of the paper. 

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Section: F Fault Indicator Based Methodsmentioning

confidence: 99%

Fault Location in Self-Healing Distribution Networks

Yin¹,

Ltd.²,

Ding³

2016

IJOEE

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“…In paper [23], precise fault location was based on the number of FTU/RTU in DA system. And the fault location method mentioned in [2,24] only fits for neutral point effectively grounded system.…”

Section: Introductionmentioning

confidence: 99%

“…In paper [1,2], artificial neural networks (ANN) was employed for building fault detection, faulted phase selection and direction estimation. Literature [3] proposed parallel transmission lines fault location algorithm by analyzing the voltage distributions.…”

Section: Introductionmentioning

confidence: 99%

Fault location in loop distribution network using SVM technology

Deng

Yuan

Xiao

et al. 2015

International Journal of Electrical Power & Energy Systems

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“…The proposed algorithm utilises both voltage magnitudes and currents from the SMs to accurately determine fault locations. The authors in [27,28] used measurements from a feeder terminal unit at each section to detect fault currents and to identify the faulted section again, and this was achieved by relying on the accuracy of the current measurement devices. Refs.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

Numair,

Aboushady,

Arraño-Vargas

et al. 2023

Energies

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Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on a densely-noded distribution network.

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Faults locations in automated distribution system

Cited by 21 publications

References 12 publications

Fault Location in Self-Healing Distribution Networks

Fault Location in Self-Healing Distribution Networks

Fault location in loop distribution network using SVM technology

Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin

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