检修电力设备集群时, 采用高效合理的检修策略是提升设备可靠性和检修效率的关键, 现有检修策略往往忽视设备个体状态差异, 无法实现设备集群中故障风险与检修成本的最优平衡。该文提出一种基于单体设备风险差异的电力设备集群检修策略, 旨在以设备集群可靠性与检修经济性最优平衡为目标, 实现单体设备的最优检修时间和顺序安排。该文首先采用结合Weibull分布和设备健康指数的变压器故障率预测方法, 预测了每台设备的故障率; 其次, 建立了电力设备集群检修时序优化模型, 并将该模型线性化为混合整数线性规划问题, 通过求解该问题得出最优检修策略; 最后, 通过实际案例对电力设备集群检修时序优化模型的可靠性权重系数进行了敏感性分析, 以评估该系数对设备集群可靠性与检修经济性平衡结果的影响。研究结果表明: 相较于传统检修策略, 优化后检修策略的检修成本和设备平均故障率分别降低了12.6%和8.2%, 验证了该文所提检修策略优化方法可有效提升电力设备集群的检修经济效益。该文研究结果可为后续电力设备集群管理研究和工程应用提供参考。

Objective: Transformers 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. Methods: To 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. Results: The 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. Conclusions: This 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.