  Measurement uncertainties, represented by cyber-attacks and data losses,
seriously degrade the quality of power system measurements. Fortunately, the
powerful generation ability of the denoising diffusion models can enable more
precise measurement generation for power system data recovery. However, the
controllable data generation and efficient computing methods of denoising
diffusion models for deterministic trajectory still need further investigation.
To this end, this paper proposes an improved two-stage denoising diffusion
model (TSDM) to identify and reconstruct the measurements with various
measurement uncertainties. The first stage of the model comprises a
classifier-guided conditional anomaly detection component, while the second
stage involves diffusion-based measurement imputation component. Moreover, the
proposed TSDM adopts precise means and optimal variances to accelerate the
diffusion generation process with subsequence sampling. Extensive numerical
case studies demonstrate that the proposed TSDM can accurately recover power
system measurements despite strong randomness under renewable energy
integration and highly nonlinear dynamics under complex cyber-physical
contingencies. Additionally, the proposed TSDM has stronger robustness compared
to existing reconstruction networks and exhibits lower computational complexity
than general denoising diffusion models.
