The Kalman filter (KF) algorithm analyses power system state estimation using a number of specific equations to reduce the mean squared error. The Kalman filter is used to calculate the dynamic states of a power system network, including voltage and its angle at all buses (rotor angle) with respect to a synchronously rotating reference frame (in radians) and relative angular speed (in rad./sec) of all the generators in the system. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) approaches were developed on the Anderson & Fouad 9-bus test power system in this proposed work, and the performance of the aforementioned techniques was examined for state estimation effectiveness.
State estimation (SE) serves as the heart of the energy management systems in providing online database by processing slow‐sampled supervisory control and data acquisition (SCADA) measurements. The recent development of fast sampled phasor measurement units (PMUs) provides real‐time measurements that enable tracking of the system state; however, the associated high initial cost does not allow to completely replace the existing SCADA measurements by PMUs. This article thus attempts to employ both the SCADA and PMU measurements, and suggests a new SE scheme comprising of a static SE module (SSEM) that estimates system state at defined time interval in the order of minutes using both the SCADA and PMU measurements, and a fast state reconstructing module for tracking the system state using PMU measurements and the calculated pseudo measurements in place of SCADA measurements. The proposed scheme employs Broyden's approach that avoids repeated formation and factorization of the Jacobian and gain matrices, respectively while iteratively solving the nonlinear simultaneous equations. It exhibits superior computational efficiency of the suggested scheme by presenting simulation results of four standard test systems.
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