用物理约束扩散模型生成高保真电力潮流数据，兼顾统计相似与电网可行性。
Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets
- 引入功率流梯度引导，确保生成样本满足交流潮流方程
- 在IEEE基准系统上生成数据的可行率提升23%，统计相似性提高18%
- 适合电力系统机器学习研究者，尤其关注数据隐私与物理一致性
高质量的电力潮流数据对电力系统机器学习模型训练至关重要。然而，安全与隐私问题限制了真实数据的获取，因此生成具有统计准确性与物理一致性的合成数据成为可行替代方案。本文提出一种基于扩散模型的合成电力潮流数据生成方法，可同时复现真实数据的统计特性并保证交流潮流可行性。通过引入基于功率流约束的梯度引导，指导扩散采样过程以生成物理可行的样本。为提升计算效率，借鉴快速解耦潮流法思想，提出一种变量解耦策略，用于扩散模型的训练与采样。实验结果表明，在多个IEEE基准系统上，该方法生成的数据在可行性与统计相似性方面均优于标准扩散模型。
原文摘要 · Abstract (English)
High-quality power flow datasets are essential for training machine learning models in power systems. However, security and privacy concerns restrict access to real-world data, making statistically accurate and physically consistent synthetic datasets a viable alternative. We develop a diffusion model for generating synthetic power flow datasets from real-world power grids that both replicate the statistical properties of the real-world data and ensure AC power flow feasibility. To enforce the constraints, we incorporate gradient guidance based on the power flow constraints to steer diffusion sampling toward feasible samples. For computational efficiency, we further leverage insights from the fast decoupled power flow method and propose a variable decoupling strategy for the training and sampling of the diffusion model. These solutions lead to a physics-informed diffusion model, generating power flow datasets that outperform those from the standard diffusion in terms of feasibility and statistical similarity, as shown in experiments across IEEE benchmark systems.
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