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Case study

LV Predict

Low Voltage (LV) underground cable network demand is set to increase significantly. A probabilistic framework will be used to determine the likelihood a cable will fail.

  • Client

    Electricity North West Ltd (ENWL)

  • Business need

    To develop a probabilistic framework to predict the current condition and risk of failure of underground low voltage (LV) cables.

Overview

The network operators have many underground low voltage (LV) cables with minimal historic data. With the electrification of heat and transport, the mean and peak demand on the network will increase significantly. It is not currently known what causes LV cables to degrade and subsequently fail: prolonged high demand, increased peak demand or the environment the cable is buried in? This is a large problem for the network as they must anticipate which LV cables are likely to fail so they and invest in new ones where required.

The aim of this project is to use a probabilistic framework to determine the likelihood a cable will fail now, and in the future, given a range of future energy scenarios. There is historically minimal and poor-quality data surrounding LV cable failure, so we develop a probabilistic framework to ensure all uncertainties are accounted for.

Once the framework is developed, we will produce an overall LV cable integrity platform which will forecast how mean and peak demand will change in the future and use this to predict the likelihood of cable failure for each scenario.

There will be two underlying models in the framework: one will determine the historic demand in through the cable from the substation demand histories, the second will be a cable degradation model built by our materials experts. The historic demand model will determine the temperature in the cable through time, including transient temperature.

The degradation model will determine how the cable degrades in different environments: for example, how deep is it buried, what soil is it buried in, is it buried near a busy road and therefore subject to higher vibrations, is it nearer a higher rodent population who may damage the insulation? These factors will be combined into the overall predictive model, alongside expert knowledge and judgement of failure causes and degradation.

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