Particle filter approach to dynamic state estimation of generators in power systems

2016 ◽  
Author(s):  
Kianoush Emami ◽  
Tyrone Fernando ◽  
Herbert Iu ◽  
Hieu Trinh ◽  
Kit Po Wong
Keyword(s):  
Power Systems ◽  
Particle Filter ◽  
State Estimation ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Filter Approach

Particle Filter Approach to Dynamic State Estimation of Generators in Power Systems

2015 ◽  
Vol 30 (5) ◽  
pp. 2665-2675 ◽  
Author(s):  
Kianoush Emami ◽  
Tyrone Fernando ◽  
Herbert Ho-Ching Iu ◽  
Hieu Trinh ◽  
Kit Po Wong
Keyword(s):  
Power Systems ◽  
Particle Filter ◽  
State Estimation ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Filter Approach

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.

An Unscented Particle Filtering Approach to Decentralized Dynamic State Estimation for DFIG Wind Turbines in Multi-Area Power Systems

2020 ◽  
Vol 35 (4) ◽  
pp. 2670-2682
Author(s):  
Samson Shenglong Yu ◽  
Junhao Guo ◽  
Tat Kei Chau ◽  
Tyrone Fernando ◽  
Herbert Ho-Ching Iu ◽  
...  
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Wind Turbines ◽  
Particle Filtering ◽  
Dynamic State ◽  
Dynamic State Estimation ◽  
Filtering Approach
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