通过约束多源信息提升配电网拓扑推断的准确性与可扩展性
Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
- 基于多源异构数据，局部修正初始拓扑并施加物理约束
- 在超过8000个智能电表上实现95%以上拓扑重建准确率
- 适合需要高可靠拓扑的电网运维与故障定位场景
精确的配电网拓扑对故障定位、电压分析及电网运行至关重要，但因用电数据异质性和不完整，保持可靠的连接记录仍具挑战。现有方法主要依赖电气相似性或空间信息，但在密集馈线和元数据不一致时不可靠。本文将拓扑识别建模为受限推理问题，利用异构证据修正电力公司提供的基础拓扑，同时满足空间合理性与物理运行约束。不从头重建连接，而是检测不一致分配，在受限邻域内进行局部重连以保证可扩展性，并迭代强化物理可行性，生成运行一致的拓扑估计。此外，提出一种基于证伪的可靠性度量，评估每条推断连接相对于其他可行方案的支持强度，帮助电力公司优先验证关键连接，同时保持全系统可观测性。该框架在与美国大型电力公司合作的三个馈线上进行了验证，覆盖超8000个AMI电表。结果表明，拓扑重建准确率超过95%，且计算开销显著低于全局推理方法。研究进一步显示，仅依赖相关性的方法在密集城区馈线中会产生模糊结果，而结合电气测量、空间与运行约束可实现稳健且可扩展的拓扑恢复。
原文摘要 · Abstract (English)
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.
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