Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. This tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.

翻译：从数据中揭示馈线拓扑结构对于提升配电网态势感知能力及合理利用智能资源至关重要。本教程系统总结、对比并建立了近期针对配电网提出的拓扑识别与检测方案之间的有用联系。重点阐述如何在克服配电网测量设备有限性的同时，通过利用潮流物理守恒定律与馈线结构特性来增强拓扑估计的方法。来自相量测量单元或智能电表的电网数据可通过传统被动方式采集，也可通过主动激励电网资源并测量馈线电压响应来获取。本文综述了在不同量测装置部署场景下关于馈线可辨识性与可检测性的分析结论。此类拓扑学习可通过具有不同计算复杂度的算法方案实现精确或近似求解，涵盖从最小二乘拟合到凸优化问题、从图上的多项式时间搜索到混合整数规划等方法。尽管研究重点集中于辐射状单相馈线，但文中亦讨论并可能拓展至环状和/或多相网络。本教程旨在为研究人员与工程师提供当前可解性配电网学习领域的前沿知识，并展望未来研究方向。