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.

翻译：电力系统信息物理不确定性，包括网络攻击与数据丢失导致的量测模糊性，以及大规模可再生能源与复杂动态特性引入的系统不确定性，降低了提升量测量质量的可能性。值得庆幸的是，去噪扩散模型对现实世界中复杂的底层物理规律展现出强大的学习与生成能力。为此，本文提出一种改进的基于检测与填补的两阶段去噪扩散模型，用于识别并重建受各类信息物理不确定性影响的量测数据。模型第一阶段包含分类器引导的条件异常检测组件，第二阶段则为基于扩散的量测填补组件。此外，所提TSDM采用最优方差策略，通过子序列采样加速扩散生成过程。大量数值案例研究表明，即使面对可再生能源引发的强随机性与高度非线性动态特性，所提TSDM仍能准确恢复电力系统量测数据。与现有重构网络相比，所提TSDM具有更强的鲁棒性，同时相较于通用去噪扩散模型展现出更低的计算复杂度。