摘要
本文将 RN 检测法与状态预估检测辨识法相结合 ,提出了一种新的不良数据的检测辨识方法。该法先用 RN检测法来判断是否有不良数据测点存在 ,若有 ,再用状态预估辨识哪些测点存在不良数据 ,将不良数据测点的量测值换成其状态预估值再进行一次状态估计。该法既克服了 RN检测法存在的残差污染、残差淹没现象 ,又解决了状态预估检测辨识法不能区分不良数据与突变量的问题。用 FOR-TRAN77语言编写程序 ,对 1 8节点系统进行了数字仿真实验 ,得到了在无不良数据、有不良数据、有突变量等情况下的检测辨识结果。
Combining R n detection with state forecast detection and identification,a new bad data detection and identification method is presented. This method can overcome the phenomenon of residual pollution and residual submersion,and can differentiate between bad data and sudden change.To program with FORTRAN77 language reference to the program frame,digital simulation test is performed in an 18 node system.The detection and identification results of simulation for no bad data,having bad data and having sudden change is obtained.The results show that the detection and identification method produced in this paper is effective.
出处
《电力系统及其自动化学报》
CSCD
2001年第2期39-43,共5页
Proceedings of the CSU-EPSA
