A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses

Abstract

In the context of smart grids, Distribution Systems State Estimation (DSSE) is notoriously problematic because of the scarcity of available measurement points and the lack of real-time information on loads. The scarcity of measurement data influences on the effectiveness and applicability of dynamic estimators like the Kalman filters. However, if an Extended Kalman Filter (EKF) resulting from the linearization of the power flow equations is complemented by an ancillary prior least-squares estimation of the weekly active and reactive power injection variations at all buses, significant performance improvements can be achieved. Extensive simulation results obtained assuming to deploy an increasing number of next-generation smart meters and Phasor Measurement Units (PMUs) show that not only the proposed approach is generally more accurate and precise than the classic Weighted Least Squares (WLS) estimator (chosen as a benchmark algorithm), but it is also less sensitive to both the number and the metrological features of the PMUs. Thus, low-uncertainty state estimates can be obtained even though fewer and cheaper measurement devices are used.

Tri-Objective Optimal PMU Placement Including Accurate State Estimation: The Case of Distribution Systems

“…where function Eig(•) returns the eigenvalues of the argument matrix. The rationale for choosing (7) as the objective function for estimation uncertainty is that, from a geometrical standpoint, the maximum eigenvalue of Φ c v represents the radius of the hypersphere circumscribing the ellipsoidal uncertainty region around the estimated state [54].…”

A novel distribution system state estimator based on robust cubature particle filter used for non‐gaussian noise and bad data scenarios

“…The estimated voltage magnitude in the distribution system obtained with the RCPF2 when there were missing measurements FIGURE 12 The estimated voltage angle in the distribution system obtained with the RCPF2 when there were missing measurements FIGURE 13 The voltage magnitude relative estimation error obtained with the RCPF2 when there were missing measurements FIGURE 14 The voltage angle absolute estimation error obtained with the RCPF2 when there were missing measurements non-Gaussian noise, at the 30th sampling instance and under the conditions of the same parameter settings. Therefore, the RCPF2 algorithm proposed in this paper can track the true values more accurately.…”

“…And a Bayesian estimator is also proposed to deal with the non‐Gaussian uncertainty of pseudo‐measurements [12]. However, distribution system state estimators tend to use algorithms based on the Kalman framework [13, 14]. The simulation results in [15] show that the cubature Kalman filter (CKF) algorithm has a high estimation accuracy and is insensitive to the scale of the system.…”

Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations

“…Due to the advantages of the FASE method and its promising prospect of practical application, it has attracted increasing attention for research, and there have been plenty of remarkable achievements made in addressing FASE problems for the ADS (Ćetenović and Ranković, 2018;Macii et al, 2020;Cheng and Bai, 2021;Geetha et al, 2021). For instance, a novel approach in assessing the process noise covariance matrix for FASE in ADS has been proposed byĆetenović and Ranković (2018), which contributes in improving the accuracy of estimation.…”