A Screening Method of Asynchronous Monitoring Data Applied to Interval Harmonic State Estimation

The measurement data of interval harmonic state estimation (IHSE) in power system is composed of harmonic monitoring data (HMD). Therefore, the quality of HMD is important to the accuracy of IHSE in power system. HMD comes from the power quality monitoring device (PQMD). PQMD detection period of different measuring points is asynchronous. This paper pointed out that the asynchronism of PQMD may cause some measurements in IHSE to deviate from the actual value and becomes bad data when the system state changes suddenly. Large amount of new energy is connected to the grid, which makes the state of the grid present strong randomness and volatility. Therefore, the application of HMD in IHSE is restricted. It is an important premise and basis to screen the bad data from the asynchronous HMD to ensure the accuracy of IHSE. According to the characteristics of sampling method and data format of PQMD, this paper pointed out that the bad data caused by the asynchronism of PQMD exists in the mutational data. To screen out the bad data, a two-step screening method of HMD applied to IHSE is proposed in this paper. Firstly, a standardized error detection method based on the self-variance variation rule is proposed. This method can screen out mutational data in HMD. Then, a data screening method based on sliding window correlation coefficient is proposed to screen the bad data in the mutational data. Finally, the effectiveness of the proposed method is verified by simulation in IEEE14-bus system.