Abstract
This study conducts a detailed a Reliability-Centered Approach to Preventive Maintenance (RCM) evaluation of an 11/0.415 kV secondary distribution substation using actual 2024 operational failure data comprising 187 recorded component failures and 1,003 outage hours across eleven major equipment categories. Reliability indices—failure rate, Mean Time between Failures (MTBF), and Mean Time to Repair (MTTR) were computed for each component. The fuses, transformer, switchgear, and outgoing feeders were identified as the most critical assets, together accounting for over 80% of total failures and approximately 85% of total outage duration, thereby forming the basis for RCM prioritization.
A Monte-Carlo simulation with 10,000 iterations was employed to evaluate three maintenance strategies: Reactive maintenance, Annual Preventive Maintenance for all components, and an RCM-based strategy targeting only the top three most critical components. The Reactive policy produced the highest annual outage duration, with an average of 1019.31 hours, corresponding to a system availability of 88.36%. Implementing Annual Preventive Maintenance across all components resulted in a significant reduction in expected outage hours to 553.64 hours, improving availability to 93.68%. The RCM-Top3 strategy, which applied preventive actions only to the transformer, switchgear, and outgoing feeders, achieved an average outage duration of 627.78 hours and availability of 92.83%, while requiring 73% fewer preventive maintenance actions compared to the full preventive maintenance plan.
The results demonstrate that preventive maintenance substantially improves substation reliability, and that an RCM-based prioritization approach delivers a favorable balance between reliability improvement and maintenance resource utilization. The study highlights the value of data-driven maintenance planning in power distribution networks, especially in resource-constrained environments. Future work should incorporate multi-year failure modeling, cost-benefit analysis, and optimization of preventive maintenance intervals to further enhance asset management strategies.