Monitoring and Fault Location Sensor Network for Underground Distribution Lines

One of the fundamental tasks of electric distribution utilities is guaranteeing a continuous supply of electricity to their customers. The primary distribution network is a critical part of these facilities because a fault in it could affect thousands of customers. However, the complexity of this network has been increased with the irruption of distributed generation, typical in a Smart Grid and which has significantly complicated some of the analyses, making it impossible to apply traditional techniques. This problem is intensified in underground lines where access is limited. As a possible solution, this paper proposes to make a deployment of a distributed sensor network along the power lines. This network proposes taking advantage of its distributed character to support new approaches of these analyses. In this sense, this paper describes the aquiculture of the proposed network (adapted to the power grid) based on nodes that use power line communication and energy harvesting techniques. In this sense, it also describes the implementation of a real prototype that has been used in some experiments to validate this technological adaptation. Additionally, beyond a simple use for monitoring, this paper also proposes the use of this approach to solve two typical distribution system operator problems, such as: fault location and failure forecasting in power cables.Ministerio de Economía y Competitividad, Government of Spain project Sistema Inteligente Inalámbrico para Análisis y Monitorización de Líneas de Tensión Subterráneas en Smart Grids (SIIAM) TEC2013-40767-RMinisterio de Educación, Cultura y Deporte, Government of Spain, for the funding of the scholarship Formación de Profesorado Universitario 2016 (FPU 2016

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

A voltage-based approach for series high impedance fault detection and location in distribution systems using smart meters

High impedance faults (HIFs) have been a major concern for protecting distribution systems and public safety hazards when involving downed conductors. The deployment of smarter grids brings new technologies for smart monitoring, automation, and protection of distribution networks. This paper presents a new method for a series of HIF detection and location in primary distribution feeders, using voltage unbalance measurements collected from smart meters (SMs) installed at low-voltage end-users. The methodology was tested in MATLAB and Simulink through steady-state simulations of a typical 13.8 kV distribution system, under load unbalance and di erent fault scenarios. Results show that the proposed method is robust and accurate for the detection of blown fuses and broken conductors, with or without ground faults, located either at the source or the load-side. The ease of implementation in SM design, formulation of parameters, and reliable simulation results show potential real-life applications

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin

Modern solutions for precise fault localization in Low Voltage (LV) Distribution Networks (DNS) often rely on costly tools such as micro-Phasor Measurement Unit (μPMU), 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. 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 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 cables' currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by industry partner Scottish Power Energy Networks (SPEN). Results show that the current estimation regressor significantly improves fault localization and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, enabling highly accurate fault detection using SM voltage-only data, with further refinement through estimation of CSC. The proposed DT offers automated fault detection, enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on the densely-noded distribution network

Fault detection and localisation in LV distribution networks using a smart meter data-driven digital twin.

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 ( PMU), 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 PMUs. 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 PMU on a densely-noded distribution network