A diagnostics framework for underground power cables lifetime estimation under uncertainty

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.