A Review on the Lifetime Estimation Methods of XLPE Power Cables

This paper presents a review of the aging mechanisms and lifetime estimation methodologies for medium and high voltage cross-linked polyethylene (XLPE) cables under harsh environmental service conditions, which are integral to the reliability and safety of modern electrical power systems. The article first briefly delves into the various aging mechanisms experienced by power cables, describing the physical and chemical processes that underlie the degradation of XLPE cable insulation over time. The discussion then extends to various life models: physical life models that describe material property changes under operational stresses; phenomenological life models and multi-stress models that consider the concurrent impact of multiple stressors on cable aging; and probabilistic and reliability lifetime models which introduce a statistical perspective to the remaining lifetime estimation, essential for risk assessment in power systems. The review also explores frequency-based life models that investigate the effects of operational frequencies on cable longevity. A significant focus is placed on enlargement laws and electrical treeing life models, shedding light on specific degradation phenomena pertinent to high voltage insulation. The paper next examines artificial intelligence (AI)-based life models, a cutting-edge approach that integrates traditional knowledge with advanced computational techniques such as machine learning and data analytics for enhanced prediction of cable life expectancy. Future research directions are also proposed in this paper, which proposes a finite element method-AI Assisted partial discharge (PD) Analysis and remaining useful lifetime (RUL) Estimation model for XLPE Cables. This comprehensive review aims to serve as an indispensable resource for engineers and researchers, offering a holistic understanding of the state-of-the-art and future directions in the domain of cable life estimation and prognostics.