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MEDIUM VOLTAGE NETWORKS AUTOMATION TO IMPROVEMENT OF THE ELECTRICAL ENERGY DISTRIBUTION SERVICE

Florin-Dumitru Andrei

First published: 2019-06-20https://doi.org/10.5593/sgem2019/4.1/s17.041View metrics

Abstract

Reducing consumers' electricity supply time, reducing downtime in selection of the medium-voltage feeder faulty branch requires that the network be equipped with commanding devices, automation and modern primary switching equipment.The medium voltage automation system will be based on the degree that, each line, contributes to attract sanctions for unplanned interruptions, and by analyzing the causes that determine the interruptions in continuity of electrical energy supply in the past two to three years.The adoption of this system allows, on the other hand, a reduction in the interruptions without giving notifications to customers by detection and isolating the faulty line section and service restoration on the healthy sections, reducing the intervention costs to isolate the fault.To enable automation system on a medium voltage distribution network, it is necessary to install reclosers along the lines, directional fault detections and the presence / absence of voltage but also to activate auto reclose cycle device on the feeder line in the transformation station.This study presents a brief presentation of the technical solutions used automation system on the medium voltage distribution networks and analyzes the results obtained by the ENEL distribution operator in Romania by applying a new solution.

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Publication details

Title
MEDIUM VOLTAGE NETWORKS AUTOMATION TO IMPROVEMENT OF THE ELECTRICAL ENERGY DISTRIBUTION SERVICE
Authors
Florin-Dumitru Andrei
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Energy and Clean Technologies
Publisher
STEF92 Technology
Year
2019
Pages
315-324
SWS Citekey
Andrei201917315324
ISSN
1314-2704
ISBN
978-619-7408-83-6
Language
en
Publication type
Conference Paper
Keywords
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