From Graphs to Feeders: Constraint-Guided Diffusion for Rule-Compliant Feeder Generation

2026-09-24

AI Summary

This study addresses the challenge of generating power grid feeders that satisfy electrical compatibility and radial topology constraints when detailed parameters are unavailable. To this end, we propose PG-DiGress, a model that pioneers the integration of domain-specific physical rules into a discrete denoising diffusion process. The method combines graph neural networks with soft-mask injection to guide sampling, and employs a topology projection algorithm for constraint reconstruction, thereby overcoming the limitation of conventional generative models that merely match statistical properties. Experimental results demonstrate that the strict rule compliance rate of generated feeders increases from 13.7% to 96.8%. Furthermore, the generated topologies can be directly applied to downstream power system analysis, achieving highly compliant and practically deployable topology generation.

Abstract

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.

Problem

• feeder generation
• constraint-guided graph generation
• distribution feeders
• electrical compatibility
• radiality rules

Innovation

• Constraint-Guided Diffusion
• Discrete Denoising Diffusion
• Feeder Generation
• Graph Generation
• Soft Masks