Resilient Dynamic State Estimation for Power System Using Cauchy-Kernel-Based Maximum Correntropy Cubature Kalman Filter

• Author(s) / Creator(s)

  • Y. Wang, Z. Yang, Y. Wang, Z. Li, V. Dinavahi, J. Liang

• Accurate estimation of dynamic states is the key to monitoring power system operating conditions and controlling transient stability. However, the inevitable non-Gaussian noise and randomly occurring denial-of-service (DoS) attacks may deteriorate the performance of standard filters seriously. To deal with these issues, a novel resilient cubature Kalman filter based on the Cauchy kernel maximum correntropy optimal criterion approach (termed CKMC-CKF) is developed, in which the Cauchy kernel function is utilized to describe the distance between vectors. Specifically, the errors of state and measurement in the cost function are unified by a statistical linearization technique, and the optimal estimated state is acquired by the fixed-point iteration method. Due to the salient thick-tailed feature and the insensitivity to the kernel bandwidth of Cauchy kernel function, the proposed CKMC CKF can effectively mitigate the adverse effect of non-Gaussian noise and DoS attacks with a better numerical stability. Finally, the efficacy of the proposed method is demonstrated on the standard IEEE 39-bus system under various abnormal conditions. Compared with standard cubature Kalman filter (CKF) and maximum correntropy criterion CKF (MCC-CKF), the proposed algorithm reveals better estimation accuracy and stronger resilience.

• Date created

  2023-04-01

• Subjects / Keywords

  • Dynamic state estimation, power system, resilience, maximum correntropy, non-Gaussian noise, DoS attacks.

• Type of Item

  Article (Published)

• DOI

  https://doi.org/10.7939/r3-bgxv-md56

• License

  Attribution-NonCommercial 4.0 International