Weather Related Fault Prediction in Minimally Monitored Distribution Networks

Eleni Tsioumpri, Bruce Stephen and Stephen D. J. McArthur
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Eleni Tsioumpri: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Bruce Stephen: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Stephen D. J. McArthur: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK

Energies, 2021, vol. 14, issue 8, 1-21

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 plants which have an 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 are shown to support fault prediction on distribution networks. Operational data are 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 the 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.

Keywords: data analytics; weather faults; fault prediction; distribution network; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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