A Kalman Filter Approach to Quasi-Static State Estimation in Electric Power Systems

Abstract

equation based on physical laws. The Kalman filter [2] is a Abstract-Static state estimation in electric power systems is four-step algorithm that consist of the following: 1) Predicting normally accomplished without the use of time-history data. This the future state from inputs, 2) calculating the Kalman gain, 3) paper presents preliminary work on the use of the discrete-time correcting the prediction with new measurements, 4) updating Kalman filter to incorporate time history into the statee corring e predict th new measurements, estimators. Transitional state equations are derived via linearization of the power flow equations. A simplified example using a decoupled real power flow solution demonstrates this

Power system state estimation based on Iterative Extended Kalman Filtering and bad data detection using normalized residual test

“…Since power system is characterized by a quasi-static system which changes slowly and steadily, a transition relationship between consecutive states could be formulated and utilized in predefined algorithms to enhance the estimation results and predict the upcoming states as well [12]. In [14], a state transition matrix with identity matrix was formulated and the work was later extended using a diagonal matrix in [10].…”

“…This could be regarded as an optimization problem, whose solutions were computed by minimizing J as shown in (12), or equivalently in (13). (13) Note i refers to the standard deviation of the measurement covariance matrix such that: (14) This is an iterative problem that began with a flat start, i.e.…”

“…Similar to SSE, a dynamic state estimation (DSE) have also been a topic of interest. Several studies discussed ways to manipulate state estimates and measurements in a dynamic manner [2], [7]- [12]. Among them, Iterative Extended Kalman Filter (IEKF) from [13] is a nonlinear technique, which provides better estimation of states under quasi-steady-state.…”

Evaluation of extended Kalman filter and particle filter approaches for quasi-dynamic distribution system state estimation

“…However, alternative estimation methods for DSSE in general have not been discussed extensively in research. In [8] and [9] the Kalman Filter method and the enhancement to Extended Kalman Filter (EKF) are proposed to incorporate time-history data in estimation problem. In [9] it is shown that the EKF methods leads to higher accuracy in estimation results compared to WLS and also improves convergence behaviour.…”

State estimation of Active Distribution Networks: Comparison between WLS and iterated kalman-filter algorithm integrating PMUs

“…More recently, methods based on the use of the Kalman filter (KF) [15] have been proposed too. In particular, Blood et al [16] evaluated three different algorithms in order to study the performance of a pseudodynamic system, whereas in [17] they combined the basic power flow equations with the load forecast to create a discrete-time dynamic model for SE. Gelagaev [18] made a comparison between the WLS and the Extended Kalman Filter (EKF) methods and proposed a first analysis of the relative importance of process and measurement covariance matrices.…”