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

Abstract

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.

Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters

“…PINN is composed of two ANNs, the output data of one ANN is the input data of the second ANN. The input vector X 1 of the first ANN is described in (10) and is composed of voltage magnitude (V i,0 ) and angle measurements (θ i,0 ) collected by PMUs installed on system buses (i = 1, . .…”

“…Technological advances over the years have allowed the development of Synchronized Phasor Measurement Systems that provide voltage and current magnitude and phase measurements in real time with time synchronization and high sampling rates acquired by Phasor Measurement Units (PMUs) installed on system buses [5]. Data from PMUs have led to the development of different and promising tools for monitoring [6][7][8][9], protecting [10][11][12] and controlling [13][14][15][16][17][18] power systems.…”