基于量测量突变检测与拓扑约束协同的不良数据检测

Bad Data Detection Based on Measurement Sudden Change Detection and Topology Constraint

电力系统状态估计过程中,常常由于残差污染和残差淹没等问题使不良数据点变得模糊,导致不良数据辨识难度增大.充分利用量测量突变检测方法,将超过某一门槛值的数据列为可疑数据;引入了电气量在网络拓扑间的约束关系,对已检出的可疑数据进行检测.根据电力网络的拓扑约束特性来确定量测量中的不良数据,以避免系统中存在多个不良数据时的漏检和误检.以某4节点电力系统为例,通过与加权残差法和标准残差法检测结果的对比,验证了基于量测量突变检测与电力网络拓扑约束协同的不良数据检测方法的有效性和可行性.

In the process of power system state estimation,the defective data points become blurred due to residual pollution and residual submergence,which makes the identification of bad data more difficult.Aiming at this problem,the correlation measure is used to detect the data which can be detected beyond the certain threshold value,and introduce the relationship between the amount of electricity in the network topology to detect the suspicious data detected.Through the topology constraint of the power network Characteristics to determine the poor measurement data in the measurement,the system further reduces the number of bad numbers when the number of missed and false detection of the possibility.With a four node power system as an example to carry out the analysis,with weighted residuals and standard residuals method,the test results show that the proposed method is effective and feasible for the identification of multiple bad data based on the combination of the abrupt change detection and the power grid correlation matrix.