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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 Name | Affiliation | 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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