Automating the Verification of the Low Voltage Network Cables and Topologies

Maizura Mokhtar, Valentin Robu, David Flynn, Ciaran Higgins, Jim Whyte, Caroline Loughran, Fiona Fulton

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Low Voltage (LV) networks are increasingly required to cope with challenges they were not designed for, requiring for more active network management (ANM). Crucially, ANM solutions require the availability of accurate network information. In practice, available data on LV networks can be incomplete, a problem often overlooked in prior ANM research. For example, in the U.K. and many developed countries, the lifetime of distribution networks assets spans several decades, with some of the available asset data gathered and maintained over many years. This can often lead to incomplete cable data being available to network operators. To overcome this, we propose a novel machine learning technique to autonomously approximate the missing cable information in LV networks. Our proposed algorithm uses a tree-based search methodology, which approximates the missing cable's cross section area (XSA) data based on rules engineers used when designing the LV networks. We validate our approach using a large database of real LV networks, where some of the cables' XSA are treated as unknown and used as ground truth to evaluate the accuracy of the predictions. Moreover, we propose a mechanism that scores the confidence level of the prediction, information which is then presented to the human network planners.

Original languageEnglish
Article number8839818
Pages (from-to)1657-1666
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume11
Issue number2
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • asset management
  • cables
  • LV networks
  • machine learning
  • network trees (graphs)

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