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

• URL: http://arxiv.org/abs/2607.20480v1
• Date: Sat, 30 May 2026 06:00:34 GMT
• Title: Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
• Abstract summary: Distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids.<n>Existing topology identification methods rely primarily on electrical similarity or spatial records alone.<n>This paper formulates distribution topology identification as a constrained inference problem that refines a utility-provided base topology.
• Score: 10.602779718858965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification [11.354716471312239]
  Domain adaptation has advanced cross-scene hyperspectral image classification, significantly improving discriminative capability in complex scenarios.<n>Conventional domain adaptation methods become impractical, severely restricting their utility in realistic remote sensing scenarios.<n>We propose a topology-aware source-free learning framework to achieve robust and accurate classification without source data.
  arXiv  Detail & Related papers  (2026-08-06T12:40:06Z)
• FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning [5.979900839748232]
  This paper proposes a framework for efficient collaboration of large models on the edge.<n>FedOPAL adapts the visual prompts as feature prompts, actively correcting the feature distribution of heterogeneous data to a linearly separable space.<n> Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs.
  arXiv  Detail & Related papers  (2026-07-09T11:28:31Z)
• Isolation-based Spherical Ensemble Representations for Anomaly Detection [60.989157958972356]
  Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring.<n>Existing unsupervised anomaly detection methods face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types.<n>We propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity.
  arXiv  Detail & Related papers  (2025-10-15T09:00:05Z)
• Evaluating the Efficiency of Latent Spaces via the Coupling-Matrix [0.5013248430919224]
  We introduce a redundancy index, denoted rho(C), that directly quantifies inter-dimensional dependencies.<n>Low rho(C) reliably predicts high classification accuracy or low reconstruction error, while elevated redundancy is associated with performance collapse.<n>We show that Tree-structured Parzen Estimators (TPE) preferentially explore low-rho regions, suggesting that rho(C) can guide neural architecture search and serve as a redundancy-aware regularization target.
  arXiv  Detail & Related papers  (2025-09-08T03:36:47Z)
• From Imperfect Signals to Trustworthy Structure: Confidence-Aware Inference from Heterogeneous and Reliability-Varying Utility Data [8.390328146420211]
  We propose a scalable framework that reconstructs a trustworthy grid topology by systematically integrating heterogeneous data.<n>The proposed framework is validated using data from over 8000 meters across 3 feeders in Oncor's service territory.
  arXiv  Detail & Related papers  (2025-08-07T19:05:19Z)
• Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems [58.95962217043371]
  We present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity.<n>Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance.<n>We propose a novel topology design approach, EIB-leanrner, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs.
  arXiv  Detail & Related papers  (2025-05-29T11:21:48Z)
• Topology-Aware Conformal Prediction for Stream Networks [68.02503121089633]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework.<n>Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Robust Networked Federated Learning for Localization [7.332862402432447]
  This paper addresses the problem of approximation, which is non-smooth in a federated setting where the data is distributed across a multitude of devices. We propose a method that adopts an $L_$-norm that a robust formulation within a distributed subgradient framework, explicitly designed to handle these obstacles.
  arXiv  Detail & Related papers  (2023-08-31T13:54:37Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.