Power system state estimation of quadrature Kalman filter based on PMU /SCADA measurements
Yan Li-me · Dianji yu kongzhi xuebao · 2014
The extended Kalman filter( EKF) method currently adopted for state estimations in power systems lacks robustness,and its precision subjects greatly to nonlinearity. In this article,we came up with a nonlinear approach-namely the quadrature Kalman filter( QKF) to perform the state estimation in power systems. The algorithm utilizes Gauss-Hermitt integration points from the perspective of statistical linear regression to greatly increase the precision of the estimation. In addition,high precision,whole network synchronized phasor measurements units( PMU) data and supervisory control and data acquisition( SCADA) data based on estabilished technology were introduced for hybrid estimation. Simulation results suggest QKF method,compared with EKF method,has higher computational precision and the overall performance of state estimation in power systems is enhanced due to the PMU data. QKF method based on hybrid measurement state estimation under the condition of normal status,and perturbation system has good estimation performance,and the estimation precision in the system after joining mixed measurement data is significantly higher than single SCADA system.