Open AccessSquare‐root‐extended complex Kalman filter for estimation of symmetrical components in power system
Author(s) -
Cui Bowen
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8642
Subject(s) - kalman filter , control theory (sociology) , extended kalman filter , square root , covariance matrix , state vector , invariant extended kalman filter , mathematics , filter (signal processing) , voltage , root mean square , convergence (economics) , matrix (chemical analysis) , computer science , algorithm , engineering , statistics , physics , artificial intelligence , geometry , control (management) , electrical engineering , materials science , classical mechanics , economic growth , economics , composite material , computer vision
The paper presents a square‐root‐extended complex Kalman filter (SRECKF) by decomposing covariance matrix with its square‐root forms to improve stability of the filter for estimating complex number. αβ transformation is used to map three‐phase instantaneous voltages in the abc phases into instantaneous voltages on the αβ axes, and a non‐linear state equation and observation equation of the three‐phase voltages are built by introducing a complex vector and defining state variables. Positive symmetrical component, negative symmetrical components, and frequency of the three‐phase voltages are estimated using traditional extended complex Kalman filter (ECKF), the estimation results show that the method proposed here are superior to traditional extended complex Kalman filter on estimation accuracy and convergence rate.