Risk-Based Asset Management Framework for Enhancing High Voltage Power Infrastructure Reliability in Saudi Energy Networks

Abstract

The electricity system in Saudi Arabia is currently being expanded and is at the same time becoming more digitally monitored, although the high-voltage transformers, substations, transmission lines, switchgear and protection systems remain long-life assets whose failure can have severe implications for reliability and economic performance. This review presents a risk-based asset management framework aimed at improving the reliability of high-voltage power infrastructure within Saudi energy networks. Evidence published between 2020 and 2025 has been brought together covering the areas of transformer condition monitoring, health indices, dissolved-gas analysis, machine learning, predictive maintenance, life-cycle planning, risk assessment and the modernisation of the Saudi grid. The review applies a structured integrative method which stresses clear eligibility criteria, critical comparison and thematic synthesis rather than making up screening statistics. The findings indicate that asset decisions can be made more defensible if condition data is transformed into a health state and then combined with the probability and consequences of failure, with uncertainty taken into account, before being linked to specific actions such as inspection, maintenance, refurbishment, replacement or continued monitoring. The proposed framework thus includes five interconnected layers: asset evidence, condition state, risk quantification, intervention decisions and portfolio governance. Special attention is paid to the conditions under which the Saudi infrastructure operates, such as high ambient temperatures, exposure to dust, rapid growth in demand, the integration of renewable energy, the geographically dispersed nature of the infrastructure and the increasing reliance on digital monitoring. The framework contributes to improved reliability by directing limited maintenance resources towards those assets that have the highest risk trajectory rather than just those that are the oldest. It also points out the need for further research in the areas of fleet-scale data quality, uncertainty calibration, interpretable artificial intelligence, cybersecurity, climate stress modelling and local validation. The approach thus offers a practical basis for utility companies that wish to implement transparent, auditable and reliability-focused asset management in line with the requirements of Saudi energy transition.