@article{LUO2026, 
author = {Jingzhi LUO and Nan ZHOU and Lingen LUO and Gehao SHENG and Xiuchen JIANG},
title = {An optimization method for maintenance decision-making of power equipment clusters balancing reliability and economic efficiency},
year = {2026},
journal = {Journal of Tsinghua University (Science and Technology)},
volume = {66},
number = {7},
pages = {1320-1328},
keywords = {power equipment, decision optimization, failure rate prediction, cluster maintenance, reliability-centered maintenance},
url = {https://www.sciopen.com/article/10.16511/j.cnki.qhdxxb.2026.26.009},
doi = {10.16511/j.cnki.qhdxxb.2026.26.009},
abstract = {ObjectiveTransformers are critical assets in power transmission and distribution networks, ensuring reliable electricity delivery and overall system stability. As transformers age, their failure probability increases, leading to higher maintenance costs and outage risks. Effective maintenance planning is therefore essential for sustaining reliability, extending service life, and mitigating failures. However, limited maintenance resources make the efficient scheduling of transformer inspections and maintenance a major challenge in asset management. Traditional reliability-centered maintenance approaches, which rely on historical data and risk matrices, focus on system-level reliability while often overlooking the individual operational characteristics of transformers. Moreover, most existing strategies optimize a single objective, without achieving a systematic balance between maintenance cost and failure risk.MethodsTo address these issues, this study proposes a cluster-based maintenance scheduling framework that explicitly considers asset heterogeneity and optimizes the trade-off between economic efficiency and reliability. The methodology integrates three components. First, a modified transformer failure rate model is developed by incorporating health index-based adjustments into a Weibull distribution, enabling individualized reliability assessments. The health index, derived from condition-monitoring data such as dissolved gas analysis indicators, provides a normalized, comprehensive condition score for each transformer. Second, the adjusted failure probabilities support asset-specific risk evaluation, allowing prioritized maintenance within each equipment cluster. The core decision variables define maintenance schedules—specifying when each asset is taken offline and serviced—while adhering to operational feasibility and utility constraints, including failure rate thresholds, health index limits, allowable maintenance windows, and resource restrictions. Third, a dual-objective optimization model, formulated as a mixed-integer linear programming problem, determines the optimal timing and sequencing of maintenance tasks. Adjustable weight parameters enable flexible trade-offs between minimizing maintenance cost and reducing failure risk.ResultsThe proposed approach was validated through this real-world case study, where simulation results showed a 12.6% reduction in total maintenance costs and an 8.2% decrease in average equipment failure risk compared with conventional methods. In addition, to analyze the impact of the maintenance coefficient on the optimization results, simulations were conducted using different coefficient values within a reasonable range while keeping other parameters constant. The results showed that larger maintenance coefficients led to poorer post-maintenance recovery, accelerated degradation, and an increase in average failure rate. Consequently, more maintenance actions were required to sustain system reliability, resulting in higher total costs. Moreover, by adjusting the reliability weight in the objective function while keeping other parameters unchanged, this study found that higher reliability weights corresponded to lower failure rates. When the reliability weight was set to 0.5, the model achieved the optimal balance between failure risk and maintenance cost, whereas overly low weights tended to maintain only the minimum acceptable maintenance intensity.ConclusionsThis study presents a comprehensive, data-driven maintenance strategy that integrates Weibull-based degradation modeling, health-index-adjusted failure prediction, and optimization-based scheduling. The flexibility of the maintenance scheme is also influenced by the scale of substation assets. When the maintenance coefficient changes, the optimization strategy may remain unchanged for smaller substations due to limited equipment quantities. In addition, the reliability weight can partially affect the optimized maintenance schedule, and tuning this parameter within a reasonable range allows utilities to obtain cost-minimized solutions while maintaining the desired reliability level. The proposed framework effectively balances reliability and economic efficiency, visualizes system-wide failure trends, and supports informed decision-making for substation asset management.}
}