Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
Quick Answer
This paper presents a constrained inference framework for scalable distribution system topology identification, achieving over 95% accuracy using heterogeneous data from 8,000 AMI meters.
Quick Take
It improves upon existing methods by enforcing spatial feasibility and operational constraints, significantly reducing computational effort compared to global inference approaches.
Key Points
- Framework refines utility-provided topology using heterogeneous evidence.
- Achieves over 95% accuracy in topology reconstruction.
- Validates with operational data from three feeders in a U.S. utility.
- Reduces computational effort compared to traditional global inference methods.
- Combines electrical measurements with spatial constraints for robust results.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reliable connectivity records remains challenging in practice due to heterogeneous and imperfect utility data. Existing topology identification methods often rely primarily on electrical similarity or spatial records alone, which become unreliable in dense feeders and under inconsistent metadata conditions. This paper formulates distribution topology identification as a constrained inference problem that refines a utility-provided base topology using heterogeneous evidence while enforcing spatial feasibility and physical operational constraints. Instead of reconstructing connectivity from scratch, the proposed framework detects inconsistent assignments, performs localized reconnection within constrained neighborhoods to ensure scalability, and iteratively enforces physical feasibility to produce operationally consistent topology estimates. In addition, a falsification-driven reliability metric evaluates how strongly each inferred connection is supported relative to alternative feasible assignments, enabling utilities to prioritize verification efforts while preserving system-wide observability. The framework is validated using operational data from three feeders comprising more than $8{,}000$ AMI meters in collaboration with a large U.S. utility. Results demonstrate over $95\%$ topology reconstruction accuracy while significantly reducing computational effort compared with global inference approaches. The study further shows that correlation-based methods alone produce ambiguous assignments in dense urban feeders, whereas combining electrical measurements with spatial and operational constraints enables robust and scalable topology recovery under realistic deployment conditions.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.20480 [cs.AI] |
| (or arXiv:2607.20480v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.20480 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haoran Li [view email]
[v1]
Sat, 30 May 2026 06:00:34 UTC (13,134 KB)
— Originally published at arxiv.org
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