大数据挖掘助力全面提升电网监控水平

SCADA系统是支撑电网调度运行的基础，监控人员主要通过此系统获取电网运行状态的一系列海量信息。随着电网规模的不断增大，监控信息海量增长。电网监控工作缺乏有效分析手段，监控人员压力大、疲于应付，不利于事故和异常信号的处理。本文采用大数据分析工具，利用SCADA系统现有的海量电网运行信息，深入探究了主变油温和负载率的关系，给出主变油温和负载率之间的变化函数，并得到重过载边界主变油温阈值；构建10kV配电线路负荷快变预警模型，结合10kV线路的15分钟负荷数据变化特点，快速判断配网线路运行状态，及时给出预警；融合外部天气等数据，建立主变重过载趋势预警模型，为电网运行潜在风险的分析识别及指导电网的科学规划建设奠定了坚实的基础。

Big data mining helps to improve power grid monitoring level

SCADA is the foundation of the power dispatching. Monitoring stuffs primarily obtain status information from this system, such as grid running state. With the increasing of the scale of the power grid, the amount of status information increases. For lacking of effective analysis methods, the monitoring stuffs are under pressure, which is disadvantageous to handling abnormal signals and accidents. By using large data analysis tool ,this paper discusses the relationship between the oil temperature and the load rate, and give the function of the main oil temperature and the load rate. Meantime, the oil temperature threshold of heavy overload is obtained. This paper also constructed a load model of 10kv distribution line. Combined with the 15-minute load data of the 10kV line, the running status of the distribution line is quickly judged and an early warning will be given. Based on weather and other data, the model of transformer load has been established. This will lay a solid foundation for analyzing and identifying potential risks in grid running, and for guiding a scientific planning of power grid.