Weather related fault prediction in minimally monitored distribution networks

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Abstract

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plant which impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data is shown to support fault prediction on distribution networks. Operational data is temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales.
Original languageEnglish
Article number2053
Number of pages21
JournalEnergies
Volume14
Issue number8
DOIs
Publication statusPublished - 7 Apr 2021

Keywords

  • data analytics
  • weather faults
  • fault prediction
  • distribution network
  • machine learning

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