Title
A Diagnostics Framework for Underground Power Cables Lifetime Estimation Under UncertaintyAuthor (from another institution)
xmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
https://ror.org/00n3w3b69https://ror.org/000x3c608
https://ror.org/04bs0e027
Version
http://purl.org/coar/version/c_ab4af688f83e57aa
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Access
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.1109/TPWRD.2020.3017951Published at
IEEE Transactions on Power Delivery Early AccessPublisher
IEEEKeywords
condition monitoring
cable diagnostics
dynamic thermal rating
uncertainty ... [+]
cable diagnostics
dynamic thermal rating
uncertainty ... [+]
condition monitoring
cable diagnostics
dynamic thermal rating
uncertainty
sensitivity [-]
cable diagnostics
dynamic thermal rating
uncertainty
sensitivity [-]
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 tas ... [+]
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 physicsof-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. [-]
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