通过约束多源推理实现配电系统中可扩展的拓扑推理
Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
- Department of Electrical, Computer and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究配电系统拓扑推理问题，提出将其作为约束推理问题，利用异构证据优化基础拓扑，通过检测不一致分配、局部重新连接等确保可扩展性，经实验验证该方法拓扑重建准确率超95%，减少计算量，能在实际条件下实现可靠拓扑恢复。
AI中文摘要：
准确的配电系统拓扑对于停电定位、电压分析和配电网运行至关重要，但由于公用事业数据的异构性和不完美性，维护可靠的连接记录在实践中仍然具有挑战性。现有拓扑识别方法往往主要依赖电气相似性或空间记录，在密集馈线和元数据不一致的情况下变得不可靠。本文将配电拓扑识别表述为一个约束推理问题，利用异构证据优化公用事业提供的基础拓扑，同时强制空间可行性和物理运行约束。该框架不是从头开始重建连接性，而是检测不一致的分配，在受限邻域内进行局部重新连接以确保可扩展性，并迭代强制物理可行性以产生运行一致的拓扑估计。此外，一个证伪驱动的可靠性指标评估每个推断连接相对于替代可行分配的支持强度，使公用事业能够在保持系统范围可观测性的同时优先进行验证工作。该框架使用来自三个馈线的运行数据进行了验证，结果表明拓扑重建准确率超过95%，与全局推理方法相比显著减少了计算量。研究还表明，仅基于相关性的方法在密集城市馈线中会产生模糊的分配，而将电气测量与空间和运行约束相结合能够在实际部署条件下实现强大且可扩展的拓扑恢复。
英文摘要：
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.
