A diagnostics framework for underground power cables lifetime estimation under uncertainty

Jose Ignacio Ignacio Aizpurua, Brian G. Stewart, Stephen D. J. McArthur, Nitin Jajware, Martin Kearns, Unai Garro, Eñaut Muxika, Mikel Mendicute

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Abstract

Power cables are critical assets for the reliable operation of the grid. The cable lifetime is generally estimated from the conductor temperature and associated lifetime reduction. However, these tasks are intricate due to the complex physics-of-failure (PoF) degradation mechanism of the cable. This is further complicated with the different sources of uncertainty that affect the cable lifetime estimation. Generally, simplified or deterministic PoF models are adopted resulting in non-accurate decision-making under uncertainty. In contrast, the integration of uncertainties leads to a probabilistic decision-making process impacting directly on the flexibility to adopt decisions. Accordingly, this paper presents a novel cable lifetime estimation framework that connects data-driven probabilistic uncertainty models with PoF-based operation and degradation models through Bayesian state-estimation techniques. The framework estimates the cable health state and infers confidence intervals to aid decision-making under uncertainty. The proposed approach is validated with a case study with different configuration parameters and the effect of measurement errors on cable lifetime are evaluated with a sensitivity analysis. Results demonstrate that ambient temperature measurement errors influence more than load measurement errors, and the greater the cable conductor temperature the greater the influence of uncertainties on the lifetime estimate.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Power Delivery
Early online date19 Aug 2020
DOIs
Publication statusE-pub ahead of print - 19 Aug 2020

Keywords

  • condition monitoring
  • cable diagnostics
  • dynamic thermal rating
  • uncertainty
  • sensitivity

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    Adaptive power transformer lifetime predictions through machine learning and uncertainty modelling in nuclear power plants

    Aizpurua, J. I., McArthur, S. D. J., Stewart, B. G., Lambert, B., Cross, J. G. & Catterson, V. M., 1 Jun 2019, In : IEEE Transactions on Industrial Electronics. 66, 6, p. 4726-4737 12 p.

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    Towards a hybrid power cable health index for medium voltage power cable condition monitoring

    Aizpurua, J. I., Stewart, B. G., McArthur, S. D. J., Jajware, N. & Kearns, M., 16 Jun 2019. 4 p.

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