Priori-sensitive resampling particle filter for dynamic state estimation of UUVs

2013 ◽  
Author(s):  
Subhra Kanti Das ◽  
Chandan Mazumdar
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Dynamic State ◽  
Dynamic State Estimation

An extended Kalman particle filter for power system dynamic state estimation

2018 ◽  
Vol 37 (6) ◽  
pp. 1993-2005 ◽  
Author(s):  
Yang Yu ◽  
Zhongjie Wang ◽  
Chengchao Lu
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Design Methodology ◽  
High Accuracy ◽  
Synchronous Machine ◽  
Dynamic State ◽  
Content Type ◽  
Dynamic State Estimation ◽  
Practical Implications ◽  
Dynamic States

Purpose The purpose of this paper is to propose an extended Kalman particle filter (EPF) approach for dynamic state estimation of synchronous machine using the phasor measurement unit’s measurements. Design/methodology/approach EPF combines the extended Kalman filter (EKF) with the particle filter (PF) to accurately estimate the dynamic states of synchronous machine. EKF is used to make particles of PF transfer to the likelihood distribution from the previous distribution. Therefore, the sample impoverishment in the implementation of PF is able to be avoided. Findings The proposed method is capable of estimating the dynamic states of synchronous machine with high accuracy. The real-time capability of this method is also acceptable. Practical implications The effectiveness of the proposed approach is tested on IEEE 30-bus system. Originality/value Introducing EKF into PF, EPF is proposed to estimate the dynamic states of synchronous machine. The accuracy of a dynamic state estimation is increased.

scholarly journals Power System Dynamic State Estimation Based on a New Particle Filter

2011 ◽  
Vol 11 ◽  
pp. 655-661 ◽  
Author(s):  
CHEN Huanyuan ◽  
LIU Xindong ◽  
SHE Caiqi ◽  
Yao Cheng
Keyword(s):  
Power System ◽  
Particle Filter ◽  
State Estimation ◽  
System Dynamic ◽  
Dynamic State ◽  
Dynamic State Estimation

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.

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