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<p>As one of the prominent methods for power-system dynamic state estimation, the cubature Kalman filter (CKF) has proved its effectiveness. However, their numerical stability, handling ability when facing unstable models, non-Gaussian noise, and large outliers are the limitations when deploying them in practical applications. In this paper, a robust CKF square-root filter based on Student’s <i>t</i> maximum mixture correntropy criterion (R-SMMC-srCKF) is proposed to overcome the above problems. The SMMC optimality criterion is designed to replace the minimum mean square error criterion. In addition, some measures to enhance numerical stability are implemented such as using pseudo-inverse matrices to replace traditional inverse matrices, and a vector exponential function is used to update the covariance matrix. Finally, the superior accuracy and strong numerical stability of the R-SMMC-srCKF algorithm were validated through IEEE-14,118 bus system state estimation under complex conditions.</p>

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Robust square-root cubature Kalman filter for power-system dynamic state estimation

  • 期刊论文

  • 发表日期：

    2026年7月10日

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  • Vuong Thuy Linh,
  • Le Ngoc Giang

摘要

As one of the prominent methods for power-system dynamic state estimation, the cubature Kalman filter (CKF) has proved its effectiveness. However, their numerical stability, handling ability when facing unstable models, non-Gaussian noise, and large outliers are the limitations when deploying them in practical applications. In this paper, a robust CKF square-root filter based on Student’s t maximum mixture correntropy criterion (R-SMMC-srCKF) is proposed to overcome the above problems. The SMMC optimality criterion is designed to replace the minimum mean square error criterion. In addition, some measures to enhance numerical stability are implemented such as using pseudo-inverse matrices to replace traditional inverse matrices, and a vector exponential function is used to update the covariance matrix. Finally, the superior accuracy and strong numerical stability of the R-SMMC-srCKF algorithm were validated through IEEE-14,118 bus system state estimation under complex conditions.