大数据挖掘助力全面提升电网监控水平

Big data mining helps to improve power grid monitoring level

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10 k V配电线路负荷快变预警模型,结合10 k V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。

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 and exhausted,This paper makes use of large data analysis tool and the existing large amount of power grid operation information of SCADA system,deeply explores the relationship between the main oil temperature and the load rate,and gives the function of the change between the main oil temperature and the load rate.The main oil temperature threshold of heavy overload boundary is obtained.The load fast change warning model of the 10 k V distribution line is constructed,and combined with the 15-minute load data of the 10 k V line,the operating status of the distribution line is quickly judged,and the early warning is given in a timely manner.Combining external weather and other data,the model of main variable overload trend warning has been established,which has laid a solid foundation for analyzing and identifying potential risks in power grid operation and guiding the scientific planning and construction of power grid.