scholarly journals The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems

Enfoque UTE ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 120-130
Author(s):  
Holger Ignacio Cevallos Ulloa ◽  
Gabriel Intriago ◽  
Douglas Plaza ◽  
Roger Idrovo
Keyword(s):  
Kalman Filter ◽  
Power Systems ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Electrical Power ◽  
The State ◽  
Dynamic State ◽  
State Variables ◽  
Electrical Power Systems ◽  
Error Index

The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately  the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes.

scholarly journals Ensemble kalman filter and particle filter-based state estimation on electrical power systems

2021 ◽  
Vol 2090 (1) ◽  
pp. 012016
Author(s):  
Holger Cevallos ◽  
Gabriel Intriago ◽  
Douglas Plaza
Keyword(s):  
Kalman Filter ◽  
Power Systems ◽  
Particle Filter ◽  
State Estimation ◽  
Ensemble Kalman Filter ◽  
Electrical Power ◽  
Dynamic State ◽  
Test Case ◽  
Electrical Power Systems ◽  
Dynamic State Estimation

Abstract In this article, a referential study of the sequential importance sampling particle filter with a systematic resampling and the ensemble Kalman filter is provided to estimate the dynamic states of several synchronous machines connected to a modified 14-bus test case, when a balanced three-phase fault is applied at a bus bar near one of the generators. Both are supported by Monte Carlo simulations with practical noise and model uncertainty considerations. Such simulations were carried out in MATLAB by the Power System Toolbox, whereas the evaluation of the Particle Filter and the Ensemble Kalman Filter by script files developed inside the toolbox. The results obtained show that the particle filter has higher accuracy and more robustness to measurement and model noise than the ensemble Kalman filter, which helps support the feasibility of the method for dynamic state estimation applications.

Distributed dynamic state estimation with extended Kalman filter

2011 ◽  
Author(s):  
Pengwei Du ◽  
Zhenyu Huang ◽  
Yannan Sun ◽  
Ruisheng Diao ◽  
Karanjit Kalsi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Dynamic State ◽  
Dynamic State Estimation

scholarly journals Comparisons on Kalman-Filter-Based Dynamic State Estimation Algorithms of Power Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 51035-51043 ◽  
Author(s):  
Hui Liu ◽  
Fei Hu ◽  
Jinshuo Su ◽  
Xiaowei Wei ◽  
Risheng Qin
Keyword(s):  
Kalman Filter ◽  
Power Systems ◽  
State Estimation ◽  
Dynamic State ◽  
Estimation Algorithms ◽  
Dynamic State Estimation
Sign in / Sign up

Export Citation Format

Share Document