Modelling Network Reliability Considering Underground Cable Hot Spot Failures

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Ageing of power network assets and increasing demand challenge the utilities to economically and securely utilise the assets. The challenge of effective utilisation of assets becomes worse under unfavourable thermal conditions, which is the case of underground power cables crossing a road. There are occasions that higher conductor temperatures (10-20°C) can be developed, with a negative effect on cable's life and failure risks when no remedial actions are taken. One common practice is cable de-rating, however, sacrificing cable's power capacity. At present, the negative impact of the unfavourable conditions and of de-rating strategies has not been addressed holistically. Hence, the combining effect on failure risks and adequacy is not effectively optimised. To address this gap and quantify the impact of hot spots on networks with various cable designs, this work integrates the thermal modelling of cable's hot spots and their associated risk of failure into a unified network reliability analysis framework. The methodology is demonstrated in a modified IEEE 14-bus network, to allow the inclusion of cable hot spots. It is found that the system performance with no remedial actions on hot spots is significantly weakened. An optimal de-rating hot spot strategy by 44% to 70% is more effective instead of complete negation (i.e. 100% de-rating factor). This proposed factor considers the holistic network-asset aspects of system adequacy, ageing and failures.
Original languageEnglish
Title of host publication2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Subtitle of host publicationLiege, Belgium 18-21 Aug. 2020
Place of PublicationNew York
PublisherIEEE
Number of pages6
ISBN (Print)9781728128221
DOIs
Publication statusPublished - Aug 2020

Keywords

  • ageing
  • asset management
  • cable hot sport mitigation
  • Monte Carlo method
  • reliability assessment

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