EnGMCC-SRCKF-based secure dynamic state estimation for cyber–physical wind energy systems under event-triggered DoS attacks

This paper presents an improved SRCKF algorithm utilizing the generalized maximum correntropy criterion (EnGMCC-SRCKF) to counteract disturbances including impulsive and Laplacian noise, measurement inaccuracies, and rapid load fluctuations. For secure dynamic state estimation in cyber–physical wind energy systems (CPWESs), the square-root cubature Kalman filter (SRCKF) employing correntropy has emerged as a prominent technique, contributing to power system operational integrity and stability. The framework incorporates a kernel constructed from generalized Gaussian distributions. Through statistical linearization, state and measurement errors are consolidated into a unified cost function, with the optimum state estimate determined via fixed-point iteration. Validation on augmented IEEE 30-, 57-, and 118-bus test networks under multiple contingency conditions confirms the method’s proficiency in dynamic state estimation. Relative to established correntropy-based algorithms, the EnGMCC-SRCKF delivers superior estimation accuracy and increased resilience.