Grid watch dog: A stream reasoning approach for lightweight SCADA functionality in low-voltage grids

Konrad Diwold, Josiane Xavier Parreira, Simon Mayer, Jack Hodges, Ralf Mosshammer, Alfred Einfalt, Darko Anicic

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

The advent of distributed energy resources in conjunction with recent developments in the context of smart embedded devices has fostered the increased integration of intelligent generators and prosumers into the energy system. As a consequence, previously passively operated grids have become highly dynamic environments, which require active monitoring and control. To allow for such monitoring and control, grid operators require lightweight SCADA-like functionality that can be flexibly adapted to dynamic grid contexts and is suitable for resource-constrained embedded systems. Grid Watch Dog enables system operators to create monitoring and control rules for low-voltage field devices on the fly, and to immediately deploy them in the field. The system combines a stream reasoner on field devices with an intuitive user interface for creating and managing rules. Grid Watch Dog has been implemented as a proof of concept in a smart grid testbed and we demonstrate its application in the context of monitoring a battery energy storage system.

Originalspracheenglisch
TitelProceedings of the 8th International Conference on the Internet of Things, IoT 2018
Herausgeber (Verlag)Association of Computing Machinery
ISBN (elektronisch)9781450365642
DOIs
PublikationsstatusVeröffentlicht - 15 Okt. 2018
Veranstaltung8th International Conference on the Internet of Things: IoT 2018 - Santa Barbara, USA / Vereinigte Staaten
Dauer: 15 Okt. 201818 Okt. 2018

Konferenz

Konferenz8th International Conference on the Internet of Things
Land/GebietUSA / Vereinigte Staaten
OrtSanta Barbara
Zeitraum15/10/1818/10/18

ASJC Scopus subject areas

  • Human-computer interaction
  • Computernetzwerke und -kommunikation
  • Maschinelles Sehen und Mustererkennung
  • Software

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