Improved Two-Stage Denoising Diffusion Recovery Model Against Power Grid Measuring Uncertainties

Uncertainties in the measuring process, represented by cyber-attacks and data losses, seriously degrade the quality of power system measurements. To address this issue, this paper proposes an improved two-stage denoising diffusion model (TSDM) to identify and reconstruct the measurements with uncertainties. The first stage of the model is a classifier-guided conditional anomaly detection component, while the second stage is a diffusion-based measurement imputation component. Moreover, the proposed TSDM adopts the optimal variance to accelerate the diffusion generation process with subsequence sampling. Extensive numerical case studies demonstrate that the proposed TSDM can accurately recover power system measurements in strong randomness and highly nonlinear dynamics with robustness and lower computational complexity.