Bad Data Detection And Identification Based On PMU In Power System

With the rapid development of power industry, the power structure and operating mode are growing complicatedly gradually, and the secure and steady operation of the power system are facing enormous challenge. Bad data detection and identification is one of the important functions of power system state estimation, which aims at removing a few bad data from the measurement data and then enhancing the reliability of state estimation.As PMU-based GPS is gradually applied in power system, it has become another important measurement data source. The main idea is to convert critical measurements to redundant measurements by improving the PMU measurements data, which the bad data on critical measurements can be detected. Therefore, the thesis mainly focuses on analyzing the bad data which may occur in the PMU fully observable system and PMU partially observable system. The main contents of the research are as follows:1. Briefly introduce and analyze the basic principles of the power system bad data detection and identification and several common methods, which are compared in the IEEE 14-bus test system.2. About PMU fully observable system, a bad data detection and identification method is gived by modifying the difference of error covariance matrix. The main contents is to detecte and identify the bad data in SCADA or PMU measurements, and numerical under different bad data conditions on IEEE-14 benchmark system verified the effectiveness.3. About PMU partially observable system, a bad data detection and identification method is gived by use of the PMU measurements to update the error error covariance matrix. The main contents is to detecte and identify the bad data of the critical measurements in SCADA, and numerical under different bad data conditions on IEEE-30 and IEEE-118 benchmark system verified the effectiveness.