Proactive Cable Replacement Planning Using Neural Weibull Proportional Hazard Modeling and Bayesian Nested Monte Carlo Simulation Under Data Deficiencies

The increasing failure rate of underground cable systems due to aging poses challenges for the reliable operation of power grids. Without proper asset management methods, the grid may not adequately support the ongoing climate-centered electrification of society. This highlights the need for strategic asset maintenance decision-making. While the existing literature includes inventive approaches for reliability modeling and maintenance strategy selection, there is still a gap in integrating detailed predictive analytics-based reliability modeling that accounts for common data deficiencies, alongside maintenance strategies that consider both reliability and cost-based performance indicators. In this paper, we present a comprehensive proactive cable replacement method using the Neural Weibull Proportional Hazard model, specifically designed to handle truncation and censoring mechanisms. The method employs a Bayesian nested Monte Carlo simulation to address the dual uncertainties associated with the probabilistic reliability model. We apply this maintenance simulation method to a case study of a real distribution system in Denmark. Our results indicate that the optimal replacement strategy reduces outage costs by up to 6.16 million DKK and can decrease the System Average Interruption Duration Index (SAIDI) by more than 50% compared to alternative replacement strategies. Finally, a sensitivity analysis of the maintenance budget shows that operators can reduce their annual budget for cable replacements by up to 50%, incurring only minor additional costs and an increase in peak SAIDI of just 1.5 minutes. The innovation lies in combining the NWPH model with Bayesian Monte Carlo simulation to handle incomplete records, quantify uncertainty, and guide cost-effective maintenance decisions.