Robust Forecasting State Estimation of Power System Based on Square Root UPF

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Robust Forecasting State Estimation of Power System Based on Square Root UPF

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[1]WANG Yaoqiang,ZHAO Kai,WANG Yi,et al.Robust Forecasting State Estimation of Power System Based on Square Root UPF[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):119-126.[doi:10. 13705/ j. issn. 1671-6833. 2023. 06. 005]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 119-126 Column: Public date: 2024-04-20

Title:: Robust Forecasting State Estimation of Power System Based on Square Root UPF

Author(s):: WANG Yaoqiang¹; 2; ZHAO Kai¹; 2; WANG Yi¹; 2; WANG Kewen¹; 2; LIANG Jun¹; 3; 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Henan Engineering Research Center of Power Electronics and Energy Systems, Zhengzhou University, Zhengzhou 450001, China; 3. Cardiff University, Cardiff CF243AA, U. K.

Keywords:: power system; unscented particle filter; robust forecasting-aided state estimation; non-positive; SRUPF

CLC:: TM744

DOI:: 10. 13705/ j. issn. 1671-6833. 2023. 06. 005

Abstract:: In order to solve the problem of poor estimation accuracy and even divergence coused by the covariance matrix of state prediction error in iterative computation of forecasting-aided state estimators, in this study, a robust forecasting-aided state estimation for power systems based on SRUPF (square root unscented particle filter) was proposed. Two mathematical methods, matrix QR decomposition and matrix Cholesky factor update were adopted, and square root technology were introduced to dynamically update the state covariance matrix, thereby maintaining the positive definiteness of the state prediction error covariance matrix. The results of testing using MATLAB showed that in the non Gaussian noise testing of IEEE 30 systems, the average root mean square error of the SRUPF voltage phase angle was 0. 09% of the corresponding test value of UPF, and the average root mean square error of the SRUPF voltage amplitude was 0. 14% of the corresponding test value of UPF. In the IEEE 57 system non Gaussian noise test, the average root mean square error of the SRUPF voltage phase angle was 0. 67% of the corresponding test value of the UPF, and the average root mean square error of the SRUPF voltage amplitude was 0. 57% of the corresponding test value of the UPF. The SRUPF proposed in this paper had a good effect on solving the problem of non positive of the covariance matrix of state prediction errors in auxiliary predictive state estimation, with high estimation accuracy and robustness.

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Last Update: 2024-04-29