Data analytics to support operational distribution network monitoring

Eleni Tsioumpri, Bruce Stephen, Neil Dunn-Birch, Stephen D.J. McArthur

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)
43 Downloads (Pure)

Abstract

The operation of distribution networks has become more challenging in recent years with increasing levels of embedded generation and other low carbon technologies pushing these towards their design limits. To identify the nature and extent of these challenges, network operators are deploying monitoring equipment on low voltage feeders, leading to new insights into fault behaviour and usage characterisation. With this heightened level of observability comes the additional challenge of finding models that translate raw data streams into outputs on which operational decisions can be based or supported. In this
paper, operational low voltage substation and feeder monitoring data from a UK distribution network is used to identify fault occurrence relations to localised meteorological data, characterise the localised network sensitivities of demand dynamics and infer the effects of embedded generation not visible to the network operator. These case studies are then used to show how additional operational context can be provided to the network operator through the application of analytics.
Original languageEnglish
Number of pages6
Publication statusPublished - 21 Oct 2018
EventIEEE PES Innovative Smart Grid Technologies Conference Europe 2018 - Sarajevo, Sarajevo, Bosnia and Herzegovina
Duration: 21 Oct 201825 Oct 2018
Conference number: 8
http://sites.ieee.org/isgt-europe-2018/

Conference

ConferenceIEEE PES Innovative Smart Grid Technologies Conference Europe 2018
Abbreviated titleISGT-E 2018
Country/TerritoryBosnia and Herzegovina
CitySarajevo
Period21/10/1825/10/18
Internet address

Keywords

  • power system fault anticipation
  • distribution network monitoring
  • data analytics

Fingerprint

Dive into the research topics of 'Data analytics to support operational distribution network monitoring'. Together they form a unique fingerprint.

Cite this