Technology Category
Constraint Satisfaction and Optimization: Distributed CSP/OptimizationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Calibration & Uncertainty Quantification
From Imperfect Signals to Trustworthy Structure: Confidence-Aware Inference from Heterogeneous and Reliability-Varying Utility Data
🤖 AI Summary
Accurately reconstructing distribution network topology from multi-source, heterogeneous, and low-quality utility data—such as GIS metadata and voltage time-series signals—remains challenging due to data uncertainty and physical inconsistency.
Method: This paper proposes a confidence-aware, end-to-end learnable inference framework that jointly models spatial layout and dynamic electrical behavior. It incorporates transformer capacity constraints and radial topology priors, soft-handles data uncertainty while hard-enforcing physical feasibility, and preserves structurally critical yet low-quality information. Crucially, it quantifies connection reliability for each branch.
Results: Evaluated on three real-world Oncor feeders (>8,000 meters), the method achieves >95% topology reconstruction accuracy, significantly improves confidence calibration, and enhances computational efficiency. It establishes a robust topological foundation for digital twins in high-uncertainty distribution grid scenarios.
Technology Category
Application Category
📝 Abstract
Accurate distribution grid topology is essential for reliable modern grid operations. However, real-world utility data originates from multiple sources with varying characteristics and levels of quality. In this work, developed in collaboration with Oncor Electric Delivery, we propose a scalable framework that reconstructs a trustworthy grid topology by systematically integrating heterogeneous data. We observe that distribution topology is fundamentally governed by two complementary dimensions: the spatial layout of physical infrastructure (e.g., GIS and asset metadata) and the dynamic behavior of the system in the signal domain (e.g., voltage time series). When jointly leveraged, these dimensions support a complete and physically coherent reconstruction of network connectivity. To address the challenge of uneven data quality without compromising observability, we introduce a confidence-aware inference mechanism that preserves structurally informative yet imperfect inputs, while quantifying the reliability of each inferred connection for operator interpretation. This soft handling of uncertainty is tightly coupled with hard enforcement of physical feasibility: we embed operational constraints, such as transformer capacity limits and radial topology requirements, directly into the learning process. Together, these components ensure that inference is both uncertainty-aware and structurally valid, enabling rapid convergence to actionable, trustworthy topologies under real-world deployment conditions. The proposed framework is validated using data from over 8000 meters across 3 feeders in Oncor's service territory, demonstrating over 95% accuracy in topology reconstruction and substantial improvements in confidence calibration and computational efficiency relative to baseline methods.
Problem
Research questions and friction points this paper is trying to address.
Reconstructs trustworthy grid topology from heterogeneous utility data
Integrates spatial layout and dynamic behavior for accurate connectivity
Handles uneven data quality with confidence-aware inference mechanism
Innovation
Methods, ideas, or system contributions that make the work stand out.
Scalable framework integrates heterogeneous grid data
Confidence-aware inference handles uneven data quality
Embedded physical constraints ensure valid topology
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