Unsupervised Impedance and Topology Estimation of Distribution Networks—Limitations and Tools

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 fun...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2019-12, Vol.11 (1)
Main Authors: Moffat, Keith, Bariya, Mohini, Von Meier, Alexandra
Format: Article
Language:English
Subjects:
Distribution networks
Effective impedance
Impedance
Kron
Noise
PMU
POWER TRANSMISSION AND DISTRIBUTION
Radial
Recursive grouping
Schur
Synchrophasor
Topology
Tree
Unsupervised learning
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Summary: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. Here, 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.
ISSN:1949-3053
1949-3061