scholarly journals An extended Kalman filter approach for real-time state estimation in multi-region MFD urban networks

2021 ◽  
Vol 132 ◽  
pp. 103384
Author(s):  
Mohammadreza Saeedmanesh ◽  
Anastasios Kouvelas ◽  
Nikolas Geroliminis
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Urban Networks ◽  
Filter Approach

scholarly journals An Extended Kalman Filter Approach for Accurate Instantaneous Dynamic Phasor Estimation

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2918 ◽  
Author(s):  
Matilde de Apráiz ◽  
Ramón Diego ◽  
Julio Barros
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Phase Angle ◽  
Frequency Estimation ◽  
Dynamic Compliance ◽  
Phasor Estimation ◽  
Filter Approach ◽  
Harmonic Components ◽  
Dynamic Phasor

This paper proposes the application of a non-linear Extended Kalman Filter (EKF) for accurate instantaneous dynamic phasor estimation. An EKF-based algorithm is proposed to better adapt to the dynamic measurement requirements and to provide real-time tracking of the fundamental harmonic components and power system frequencies. This method is evaluated using dynamic compliance tests defined in the IEEE C37.118.1-2011 synchrophasor measurement standard, providing promising results in phasor and frequency estimation, compliant with the accuracy required in the case of off-nominal frequency, amplitude and phase angle modulations, frequency ramps, and step changes in magnitude and phase angle. An important additional feature of the method is its capability for real-time detection of transient disturbances in voltage or current waveforms using the residual of the filter, which enables flagging of the estimation for suitable processing.

scholarly journals Adaptive filter based on Monte Carlo method to improve the target tracking in radar systems

2020 ◽  
Author(s):  
Khaireddine Zarai ◽  
Cherif Adnane
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Research Problem ◽  
Radar System ◽  
The State ◽  
True Path ◽  
Random Target

Abstract The state estimation and tracking of random target is an attractive research problem in radar system. The information received in the radar receiver was reflected by the target, that it is received with many white and Gaussian noise due to the characteristics of the transmission channel and the radar environment. After detection and location scenarios, the radar system must track the target in real time. We aim to improve the state estimation process for too random target at the given instant in order to converge to the true target state and smooth their true path for a long time, it simplifies the process of real-time tracking. In this framework, we propose a new approach based on the numerical methods presented by MONTE CARLO (MC) counterpart the method conventionally used named Extended KALMAN Filter (EKF), we showed that the first are more successful. Keywords: Radar, Monte Carlo, Extended KALMAN Filter, Tracking, PF, Random target.

Localized Extended Kalman Filter for Scalable Real-Time Traffic State Estimation

2012 ◽  
Vol 13 (1) ◽  
pp. 385-394 ◽  
Author(s):  
Chris P. I. J. van Hinsbergen ◽  
Thomas Schreiter ◽  
Frank S. Zuurbier ◽  
J. W. C. van Lint ◽  
Henk J. van Zuylen
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Traffic State ◽  
Real Time Traffic ◽  
Traffic State Estimation
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