拓扑和导纳估计：精度限制和算法
Topology and Admittance Estimation: Precision Limits and Algorithms
摘要：
配电网拓扑和导纳信息是系统规划、运行和保护的基础。在许多配电网中，丢失或不准确的拓扑和导纳数据要求有效的估计方法。然而，测量数据可能不足或被大噪声污染，这将对估计精度引入基本限制。这项工作探讨了理论精度限制的拓扑和导纳估计（TAE）的问题，不同的测量设备，噪声水平，和测量的数量。在此基础上，我们提出了一种保守的渐进自适应（CPS）算法来估计的拓扑结构和导纳。IEEE 33和141节点系统的结果验证了所提出的CPS方法可以在各种测量设置下接近理论精度极限。
英文摘要：
Distribution grid topology and admittance information are essential for system planning, operation, and protection. In many distribution grids, missing or inaccurate topology and admittance data call for efficient estimation methods. However, measurement data may be insufficient or contaminated with large noise, which will introduce fundamental limits to the estimation accuracy. This work explores the theoretical precision limits of the topology and admittance estimation (TAE) problem, with different measurement devices, noise levels, and the number of measurements. On this basis, we propose a conservative progressive self-adaptive (CPS) algorithm to estimate the topology and admittance. Results on IEEE 33 and 141-bus systems validate that the proposed CPS method can approach the theoretical precision limits under various measurement settings.
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