基于改进粒子群算法的牵引变电所维修优化研究

An improved particle swarm algorithm study on optimization model of maintenance schedules for railway traction substations

牵引变电所维修优化方案的合理制定是保证铁路电力运输重要的一部分,维修要兼顾系统的可靠性和维修的经济性。采用故障树法、GO法对牵引变电所进行定性分析和定量分析,得到了其定性维修策略和定量可靠度的推导公式。以文献中调研数据为基础,对周期性维修策略进行改进,建立了考虑牵引变电所维修经济性和满足系统可靠度的维修优化模型,根据改进粒子群算法求解模型,制定分别从考虑单个设备可靠度和整体可靠度两个方面的维修优化策略。仿真结果表明,基于改进粒子群算法的牵引变电所维修优化模型是客观有效的。该模型能反映系统可靠度越高,维修费用越高的特性,并且可根据不同维修费用限制选择系统可靠度的最低值。

A reasonable maintenance schedule for railway traction substations is an important part of railway electric transports. It should take the reliability and the maintenance costs of the system into account. The fault tree analysis is applied to qualitative analysis and the GO method is used to quantitative analysis. Formula derivation of the railway substations’ reliability is got. Simulating the model through the survey data from the literature and improving the periodic maintenance means, establishing an optimized maintenance model of the railway traction substations based on reliability and the cost. Through two aspects of single equipment reliability or overall reliability, the model is solved with improved particle swarm algorithm. The result indicates that the model is objective and effective, that the model can reflect that the reliability of the system is higher, the maintenance costs are higher. And the minimum reliability of the system can be selected according to different maintenance cost.