Fault classification and location identification in a smart DN using ANN and AMI with real‐time data

Usman, Muhammad; Ospina, Juan; Faruque, M. Omar

doi:10.1049/joe.2019.0896

Cited by 9 publications

(1 citation statement)

References 23 publications

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“…However, the proper study of utilising SMs data for fault identification is lacking, with existing studies often impractical in comparison to realistic smart meter capabilities or due to the assuming of continuous measurements post-de-energisation, which is not the case [24]. References [25,26] present an Artificial Neural Network (ANN)-based fault location method for the IEEE-13 bus and IEEE-37 bus systems, in which the data from Advanced Metering Infrastructure (AMI) was employed. The proposed algorithm utilises both voltage magnitudes and currents from the SMs to accurately determine fault locations.…”

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%