多不良数据补偿辨识法用于电力系统状态估计

分析和比较各种不良数据检测和辨识方法和优缺点，利用综合优化法，假设试验和估计似然法，提出一种多不良数据补偿辨识的新方法，为节省内存和提高不良数据检测与辨识的计算速度，本方法把矩阵稀疏存贮和Ｔｉｎｎｅｙ－Ｗａｌｋｅｒ结点优化法结合在一起，在矩阵ＬＤＬ＾Ｔ分解中产生的填元数量少，提高了状态估计软件的实时性，通过ＩＥＥＥ－３０结点系统和天津电力系统调度中心实时试验证实了本法的有效性。

A MULTIPLE BAD DATA COMPENSATION IDENTIFICATION METHOD AND ITS APPLICATION IN POWER SYSTEM STATE ESTIMATION

n this paper, the advantage and weaknesses of all methods of bad data detection and identification are analysed and compared in theory and application. A new multiple bad data comPensation identification method is presented. Then the statistical characters of compensated measurements are analysed. Because this method utilizes the advantages of the combination optimization, hypothsis tasting identification and estimation approach, so it can deal with the multiple and interacting bad data in the power system state estimation. In order to save the storage and accelerate the speed of the bad data detection and identification,the spare matrix storage techniques and imney-Walker optimal ordering method are combined in the approach. So the number of the fill-ins can be minimized during matrix LDLT decomposing. The real-time demands of the power system state estimation can be met. The effectiveness of the approach is demonstrated via numerical examples simulated on the IEEE-30 bus system and the real-time implementation of Tianhn power system control centre, its good performance in speed and quality is confirmed.