Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties

TL;DR

This paper introduces a two-stage denoising diffusion model that effectively detects and reconstructs power system measurements affected by cyber-physical uncertainties, enhancing accuracy and robustness in complex, renewable-rich environments.

Contribution

It proposes a novel two-stage diffusion model with anomaly detection and measurement imputation, improving recovery accuracy and computational efficiency under cyber-physical uncertainties.

Findings

Accurately recovers measurements despite renewables-induced randomness.

Demonstrates robustness over existing reconstruction methods.

Achieves lower computational complexity than standard diffusion models.

Abstract

Power system cyber-physical uncertainties, including measurement ambiguities stemming from cyber attacks and data losses, along with system uncertainties introduced by massive renewables and complex dynamics, reduce the likelihood of enhancing the quality of measurements. Fortunately, denoising diffusion models exhibit powerful learning and generation abilities for the complex underlying physics of the real world. To this end, this paper proposes an improved detection and imputation based two-stage denoising diffusion model (TSDM) to identify and reconstruct the measurements with various cyber-physical 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 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 despite renewables-induced strong randomness and highly nonlinear dynamics. Additionally, the proposed TSDM has stronger robustness compared to existing reconstruction networks and exhibits lower computational complexity than general denoising diffusion models.