A Fault Diagnosis Method of Distribution Network based on Intelligent Algorithm

Du, Renjie

doi:10.1109/icdcece57866.2023.10150812

Cited by 1 publication

(1 citation statement)

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“…The literature covers numerous examples of using µPMUs for fault localisation. The authors of [18] developed a µPMU-based fault detection technique for DNs, thereby utilising a combination of voltage deviation and power change indices for fault detection in multi-configured networks. The researchers in [19] introduced an integrated fault detection, classification, and section identification, one that utilises µPMUs precise synchronised measurements for the effective pinpointing and categorising of various types of faults within a distribution grid.…”

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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Section: Introductionmentioning

confidence: 99%