铁路智能变电站变压器状态数据挖掘研究

Study on Transformer Condition Data Mining in Railway Intelligent Substation

针对高速铁路电力能源大量消耗、变压器用电安全、寿命状态管理等难题,提出一种基于数据计算建模的铁路智能变电站变压器温升仿真计算,以评估其使用寿命损失和容量优化配置情况。同时结合数据挖掘模型设定、流程分析,研究变压器随机性负荷事件与其寿命风险影响程度的关系;通过训练数据挖掘模型、数据校验,判断变压器运行状态和动态巡视检修的最佳时间,及时进行风险控制;根据事故状态指导巡视检修工作,实现由原先的计划检修转变为动态检修,减少人员传统巡检工作量。为高速铁路变压器寿命健康管理提供技术支撑,有助于推动铁路电力行业设备动态巡检的发展。

Aiming at the problems, such as high power consumption of high speed railway, safe operation of transformer, life state management, a kind of railway intelligent substation transformer temperature rise simulation is proposed based on data calculation and modeling to evaluate the service life loss and optimal allocation of capacity. At the same time, combined with data mining model setting and process analysis, the relationship between transformer random load events and life risk impact degree is studied. Through training data mining model and data verification, the optimal time of transformer operation state and dynamic inspection and overhaul is judged, and risk control is carried out in time. According to the condition of the accident, the inspection and maintenance work is conducted, and the original plan maintenance is changed into dynamic maintenance, and the traditional inspection workload is reduced. The paper provides technical support for the life health management of high-speed railway transformer, it is helpful to promote the development of dynamic inspection of equipment in railway power industry.