从图到馈线：约束引导扩散用于合规馈线生成
From Graphs to Feeders: Constraint-Guided Diffusion for Rule-Compliant Feeder Generation
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中文总结 AI 辅助
针对馈线生成需满足电气与辐射状规则的问题，提出约束引导扩散模型PG-DiGress，通过软掩码注入约束，将严格通过率从13.7%提升至96.8%。
中文摘要 AI 辅助
生成建模方法通常侧重于从训练数据中恢复广泛的统计特征。在图生成的背景下，这可能指度分布、聚类系数或谱性质。然而，当详细的馈线模型不可用时，生成可用的配电馈线不仅需要匹配通用的图统计量：采样的拓扑还必须遵循电气兼容性和辐射状规则。因此，我们将馈线合成表述为一个约束引导的图生成问题，并提出了电力网约束离散去噪扩散模型PG-DiGress，该模型从馈线数据中学习分类的节点和边模式，同时尊重领域特定的规则。具体来说，它通过软掩码将馈线约束注入反向扩散过程，在去噪期间抑制不兼容的边类别，随后通过最终投影步骤重建一个连通的、符合规则的馈线图。我们使用图分布相似性、馈线规则满足度、结构有效性和下游模型构建来评估PG-DiGress。与无约束基线相比，PG-DiGress将严格馈线通过率从13.7%提高到96.8%。我们还成功地将生成的图转换为可执行的馈线模型，用于下游分析。
英文摘要
Generative modeling approaches often focus on recovering broad statistical characteristics from the training data. In the context of graph generation, this may refer to degree distributions, clustering coefficients, or spectral properties. However, generating usable distribution feeders when detailed feeder models are unavailable requires more than matching generic graph statistics: the sampled topology must also obey electrical compatibility and radiality rules. We therefore formulate feeder synthesis as a constraint-guided graph generation problem and propose the Power-Grid-constrained Discrete Denoising Diffusion model, PG-DiGress, which learns categorical node and edge patterns from feeder data, while respecting domain-specific rules. Specifically, it injects feeder constraints into the reverse diffusion process through soft masks that suppress incompatible edge classes during denoising, followed by a final projection step that rebuilds a connected, rule-compliant feeder graph. We evaluate PG-DiGress using graph-distribution similarity, feeder-rule satisfaction, structural validity, and downstream model construction. Compared with the unconstrained baseline, PG-DiGress increases the strict feeder pass rate from 13.7% to 96.8%. We also successfully convert the generated graphs into executable feeder models for downstream analysis.
发表机构
- National Laboratory of the Rockies（落基山国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
