Unsupervised Impedance and Topology Estimation of Distribution Networks-Limitations and Tools
Unsupervised Impedance and Topology Estimation of Distribution Networks-Limitations and Tools
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DOI：
10.1109/tsg.2019.2956706
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发表时间：
2020-01-01
影响因子：
9.6
通讯作者：
von Meier, Alexandra
中科院分区：
文献类型：
--
作者：
Moffat, Keith;Bariya, Mohini;von Meier, Alexandra
分销网络模型通常不准确或不存在。这项工作考虑了在没有任何先前网络信息的情况下，根据节点电压和电流注入的噪声同步相量测量来估计配电网络的阻抗和拓扑的问题。我们证明了电气网络无监督估计的基本限制，建立了有源节点之间的有效阻抗作为核心、普遍可获得的网络信息。我们提出了一种抗噪声技术，通过 Kron 导纳矩阵的简化拉普拉斯形式（称为“subKron”形式）来估计有效阻抗。我们提出了复杂递归分组算法来根据有效阻抗重建径向网络。对噪声数据的仿真结果证明了所提出的方法对于小型网络的有效性，以及将它们应用于大型网络的挑战。随着信噪比的降低，对估计和重建精度的评估突出了噪声测量对无监督网络估计性能的基本权衡。
Distribution network models are often inaccurate or nonexistent. This work considers the problem of estimating the impedance and topology of distribution networks from noisy synchronized phasor measurements of nodal voltages and current injections, without any prior network information. We prove fundamental limits for unsupervised estimation of electrical networks, establishing effective impedance between active nodes as the core, generally-attainable network information. We propose a noise-robust technique for estimating effective impedances via the reduced Laplacian form of the Kron reduced admittance matrix, termed the "subKron" form. We present the Complex Recursive Grouping algorithm to reconstruct radial networks from effective impedances. Simulation results on noisy data demonstrate the efficacy of the proposed methods for small networks, and the challenges of applying them to large networks. Evaluations of estimation and reconstruction accuracy with decreasing signal to noise ratio highlight fundamental tradeoffs in unsupervised network estimation performance from noisy measurements.