Active Distribution System State Estimation: Comparison Between Weighted Least Squares and Extended Kalman Filter Algorithms

2020 ◽  
Author(s):  
Jeff Kimasere Watitwa ◽  
Kehinde O. Awodele
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Distribution System ◽  
Weighted Least Squares ◽  
System State ◽  
Active Distribution System ◽  
Filter Algorithms

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

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6054
Author(s):  
David Macii ◽  
Daniele Fontanelli ◽  
Grazia Barchi
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Extended Kalman Filter ◽  
Distribution System ◽  
Smart Grids ◽  
Distribution Systems ◽  
Reactive Power ◽  
Weighted Least Squares ◽  
Measurement Data ◽  
Flow Equations

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.

Active distribution system state estimation incorporating photovoltaic generation system model

2020 ◽  
Vol 182 ◽  
pp. 106247
Author(s):  
Zhi Fang ◽  
Yuzhang Lin ◽  
Shaojian Song ◽  
Chunning Song ◽  
Xiaofeng Lin ◽  
...  
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
System Model ◽  
System State ◽  
Generation System ◽  
Photovoltaic Generation ◽  
Active Distribution System

scholarly journals Adaptive Extended Kalman Filter with Correntropy Loss for Robust Power System State Estimation

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 293 ◽  
Author(s):  
Zhiyu Zhang ◽  
Jinzhe Qiu ◽  
Wentao Ma
Keyword(s):  
Kalman Filter ◽  
Power System ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
System State ◽  
Power System State Estimation ◽  
Current Operation ◽  
Adaptive Extended Kalman Filter ◽  
Non Gaussian ◽  
Operation Status

Monitoring the current operation status of the power system plays an essential role in the enhancement of the power grid for future requirements. Therefore, the real-time state estimation (SE) of the power system has been of widely-held concern. The Kalman filter is an outstanding method for the SE, and the noise in the system is generally assumed to be Gaussian noise. In the actual power system however, these measurements are usually disturbed by non-Gaussian noises in practice. Furthermore, it is hard to get the statistics of the state noise and measurement noise. As a result, a novel adaptive extended Kalman filter with correntropy loss is proposed and applied for power system SE in this paper. Firstly, correntropy is used to improve the robustness of the EKF algorithm in the presence of non-Gaussian noises and outliers. In addition, an adaptive update mechanism of the covariance matrixes of the measurement and process noises is introduced into the EKF with correntropy loss to enhance the accuracy of the algorithm. Extensive simulations are carried out on IEEE 14-bus and IEEE 30-bus test systems to verify the feasibility and robustness of the proposed algorithm.

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