# Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference

arXiv: https://arxiv.org/abs/2607.20480 · HTML: https://mlsanity.com/paper/2607.20480/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 30 May 2026.

## Builds on (most cited first, 5 of 5)

- [ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics](https://mlsanity.com/paper/2505.17488/) (2025)
- [Solar Photovoltaic Assessment with Large Language Model](https://mlsanity.com/paper/2507.19144/) (2025)
- [Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets](https://mlsanity.com/paper/2510.04138/) (2025)
- [Review of Core Process Representation in Power System Operational Models: Gaps, Challenges, and Opportunities for Multisector Dynamics Research](https://mlsanity.com/paper/2101.02303/) (2021)
- [Data quality challenges in existing distribution network datasets](https://mlsanity.com/paper/2308.00487/) (2023)

## Abstract (arXiv)

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.