Dynamic State Estimation of Power System Using Gauss-Seidel Cubature Kalman Filter

Real-time Dynamic State Estimation (DSE) is one of the most important applications of Wide Area Measurement System (WAMS) and plays a critical role in power system reliability. DSE gathers data from Phasor Measurement Units (PMU) into the Phasor Data Concentrator (PDC) and estimates the state variables of the generators. Due to strong nonlinearity of power system dynamic model, a nonlinear state estimator is required to capture system dynamics, effectively. However, the existing nonlinear estimators such as Particle Filter (PF) and Cubature Kalman Filter (CKF) may fail at real-time DSE of power systems, since they have high computational complexity. To reduce computational time of state estimation and preserve estimation performance, a nonlinear power system state estimator is developed based on Gauss-Seidel CKF (GSCKF) in this paper. Performance of the proposed method is evaluated using the standard IEEE 68-bus system with PMUs.