To solve the complex scenarios

such as multimodal noise

bad measurement data and sudden load changes

in the power system

the enhanced generalised cross correlation entropy unscented Kalman filter (EnGCCE-UKF) method is proposed in this paper. This method replaces the mean square error (MSE) criterion of the traditional UKF with the generalised cross correlation entropy (GCCE) criterion and obtains the optimal solution by optimising the state estimation cost function

significantly improving the robustness and estimation accuracy in the non-Gaussian noise environment. Considering the interference of bad measurement data on the information matrix

the strong filtering tracking (SFT) theory is further embedded in the GCCE-UKF framework. By dynamically adjusting the information matrix to suppress the influence of abnormal factors

the anti-interference ability of the algorithm is enhanced. This method integrates the state and measurement error into the cost function of the EnGCCE by using the statistical linearisation technique and recursively updates the posterior estimation and covariance matrix with the help of the fixed-point iterative algorithm. Verified through multiscenario experiments and comparative analysis

the proposed method has shown good effectiveness in power systems of three scales.