用物理约束生成真实电网功率流数据，解决隐私与数据不足问题。
Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models
- 基于去噪扩散模型，加入物理规律损失函数
- 在IEEE 14/30节点系统上生成数据，保持分布特性与可行性
- 比三种基线模型更准确、多样且符合物理约束
智能电网中的许多数据驱动模块依赖高质量的功率流数据，但受隐私和运行限制，真实数据往往有限。本文提出一种基于去噪扩散概率模型（DDPM）的物理信息生成框架，通过辅助训练与物理信息损失函数，确保生成数据兼具统计保真度与电力系统可行性。在IEEE 14节点和30节点基准系统上评估，结果表明该方法能有效捕捉关键分布特性，并泛化至分布外场景。对比实验显示，所提模型在可行性、多样性及统计特征准确性方面均优于三种基线模型。本工作展示了生成模型在数据驱动电力系统应用中的潜力。
原文摘要 · Abstract (English)
Many data-driven modules in smart grid rely on access to high-quality power flow data; however, real-world data are often limited due to privacy and operational constraints. This paper presents a physics-informed generative framework based on Denoising Diffusion Probabilistic Models (DDPMs) for synthesizing feasible power flow data. By incorporating auxiliary training and physics-informed loss functions, the proposed method ensures that the generated data exhibit both statistical fidelity and adherence to power system feasibility. We evaluate the approach on the IEEE 14-bus and 30-bus benchmark systems, demonstrating its ability to capture key distributional properties and generalize to out-of-distribution scenarios. Comparative results show that the proposed model outperforms three baseline models in terms of feasibility, diversity, and accuracy of statistical features. This work highlights the potential of integrating generative modelling into data-driven power system applications.
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