Power system state estimation comparison of Kalman filters with a new approach
TL;DR: In this paper, the performance of four filtering approaches in estimating dynamic states of a power system network using PMU data is compared under the statistical framework, Efficiency of Interpolation, Sensitivity of the missing data with computation time.
Abstract: The Synchronous machine angle and speed variables availability give us an exact picture of the overall condition of power system networks leading therefore to an improved situational cognizance by system operators. As the enlargement of power systems continue, the challenge of real time monitoring and its control gets bigger. All significant network parameters are measured at various points on the system and that collected data transferred to the control center, where the data is used for Estimation Process through various Energy Management System (EMS) functions. Accuracy is directly dependent upon the data collected through Phasor Measurement Unit (PMU) with their underlying high veracity and the unique ability to evaluate the voltage angles, offers an advantage in improving the overall accuracy. This paper compares the performance of four filtering approaches in estimating dynamic states of a power system network using PMU data. The five methods are extended Kalman filter (EKF), unscented Kalman filter (UKF), Particle Filter (PF), and Enhanced unscented Kalman filter. The statistical performance of each algorithm is compared using IEEE 30 and IEEE 14 bus test system. Under the statistical framework, Efficiency of Interpolation, Sensitivity of the missing data with computation time are evaluated and compared for each approach. According to the comparison, this paper makes some recommendations for the proper use of the methods.
...R̂ = ⎢⎢⎣ R̂z,1 · · · 0 ⋮ ⋱ ⋮ 0 · · · R̂z,⌢m ⎥⎥⎦ , (22)...