基于大数据多元电网动态参数应用的研究

Research on application of dynamic parameters of multi-grid based on big data

针对智能电网测量装置工作中出现的海量数据分析和处理速度慢、精度低等问题,本文深入研究了基于大数据的输电线路参数估计和变压器运行状态估计的方法,利用云计算的方式快速处理海量的数据,通过k-means聚类算法得出在微气象的最佳输电线路参数,建立气象环境和结构参数的在线评估系统,可对实时变化的输电线路参数进行监控及修正。解决了传统静态参数对环境突变不敏感的问题。利用变压器状态运行参数及变压器输出负荷判断变压器温升,通过变压器数学模型计算得到变压器结构参数,结合变压器输出负荷判断变压器温升,从而计算变压器绕组温度,再利用变压器绕组温度判断变压器是否处于故障运行状态。该方法符合电网运行实际,在动态参数发生变化时,及时为运行人员告警。

In view of the problems of slow speed and low precision of massive data analysis and processing in smart grid measurement devices,the methods of transmission line parameter estimation and transformer operation state estimation based on big data are deeply studied in this paper,cloud computing is used to process massive data quickly,k-means clustering algorithm is used to get the best transmission line parameters in micro-meteorology,and an on-line evaluation system of meteorological environment and structure parameters is es tablished,real-time transmission line parameters can be monitored and corrected.The problem that traditional static parameters are not sensitive to sudden change of environment is solved.The transformer temperature rise is judged by the operation parameters of transformer state and the output load of transformer,the transformer structure parameters are calculated by the transformer mathematical model,and the transformer temperature rise is judged by the transformer output load,then the winding temperature of trans:former is calculated,and then the winding temperature of transformer is used to judge whether the transformer is in fault operation state.This method accords with the actual operation of power network,and gives the alarm to the operator when the dynamic parameters change.