Dynamic harmonic state estimation of a power system based on adaptive SRUKF
DOI:10.19783/j.cnki.pspc.220514
Key Words:dynamic harmonic state estimation  square root unscented Kalman filter  noise estimation  abnormal data correction
Author NameAffiliation
ZHANG Ming 1. School of Electronic and Electrical Engineering,Wuhan Textile University, Wuhan 430200, China
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China 
XU Shilu 1. School of Electronic and Electrical Engineering,Wuhan Textile University, Wuhan 430200, China
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China 
LU Dongliang 1. School of Electronic and Electrical Engineering,Wuhan Textile University, Wuhan 430200, China
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China 
XIA Ruoping 1. School of Electronic and Electrical Engineering,Wuhan Textile University, Wuhan 430200, China
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China 
HE Shunfan 1. School of Electronic and Electrical Engineering,Wuhan Textile University, Wuhan 430200, China
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China 
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Abstract:Given the shortcomings of the traditional unscented Kalman filter (UKF) algorithm of harmonic state estimation with time-varying noise and abnormal data, an algorithm based on adaptive square root unscented Kalman filter (SRUKF) is proposed for power system harmonic state estimation. First, an improved Sage-Husa noise estimation method is proposed for real-time estimation of noise covariance in view of the time-varying noise. Second, an abnormal data correction method is proposed in view of the abnormal data interference. A correction coefficient is introduced to reduce the influence of abnormal data in state estimation. Finally, an IEEE14-node system is built to validate the estimation performance of the adaptive SRUKF algorithm. It has been applied to the dynamic harmonic state estimation of a power system. The simulation results show that the proposed algorithm has good estimation performance with the interference of time-varying noise and abnormal data. This work is supported by the National Natural Science Foundation of China (No. 61903384 and No. 51477124).
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