A New Power System State Estimation Method Based on External Student zed Residuals

3rd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Huang, Sijia^1 ; Tan, Weipu^1

School of Electrical and Electronic Engineering, North China Electric Power University, Beijing

102206, China^1

关键词: Analysis and controls;    Detection and identifications;    Power grid dispatching;    Power system online monitoring;    Power system state estimation;    Robust estimation method;    Studentized residuals;    Weighted least squares;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/300/4/042086/pdf
DOI  :  10.1088/1755-1315/300/4/042086

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Power system state estimation is the core of power system online monitoring, analysis and control function, playing an important role in the intelligent analysis and decision-making of power grid dispatching. The estimation results directly affect the correctness and effectiveness of the operation analysis and decision system, how to improve the accuracy of state estimation is an important content of the research. Detection and identification of bad data is one of the important function of state estimation in power system, its purpose is to eliminate a few bad data by the measurement data and improve the reliability of state estimation, which is of great significance to the safe operation of power system. External studentized residuals are used as criterion to detect and identify bad data in power system state estimation, improving the shortcomings of standardized residuals in traditional bad data detection and identification. Then the exponential function weighted least squares method based on external studentized residuals is proposed. The form of the original algorithm is modified using the principle of equivalent weight. Simulation is carried out according to the different situation of bad data to verify its performance. The performance is compared with the traditional robust estimation method and basic weighted least squares method.