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Regular Paper | Open Access

Partial Correlation Analysis Based Identification of Distribution Network Topology

Yanli Liu( ), Peng Wang
School of electrical and information engineering, Tianjin University, Tianjin 300072, China
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Abstract

Accurately identifying distribution network topology, which tends to be a mesh configuration with increasing penetration rate of distributed energy resources (DERs), is critical for reliable operation of a smart distribution network. Multicollinearity among node voltages makes existing topology identification methods unstable and inaccurate. Considering partial correlation analysis can reveal the intrinsic correlation of two variables by eliminating the influence of other variables, this paper develops a novel data-driven method based on partial correlation analysis to identify distribution network topology (radial, mesh, or including DERs) using only historical voltage amplitude data. First, maximum spanning tree of network is generated through Prim algorithm. Then, the loops of network are identified by taking tree neighbors as controlling variables in partial correlation analysis. Finally, a new topology verification mechanism based on partial correlation analysis is developed to correct wrong connections caused by multicollinearity. Test results on IEEE 33-node system, IEEE 123-node system and practical distribution network demonstrate that our method outperforms common data-driven methods, and can robustly identify both radial and mesh distribution network with DERs.

Keywords

Data-drivensmart meterlinear correlationpartial correlationtopology identification

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CSEE Journal of Power and Energy Systems
Volume 9 Issue 4,
July 2023
Pages 1493-1504
DOI: 10.17775/CSEEJPES.2021.08360
Cite this article:
Liu Y, Wang P. Partial Correlation Analysis Based Identification of Distribution Network Topology. CSEE Journal of Power and Energy Systems, 2023, 9(4): 1493-1504. https://doi.org/10.17775/CSEEJPES.2021.08360

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Received: 10 November 2021
Revised: 14 December 2021
Accepted: 28 December 2021
Published: 25 January 2023
© 2021 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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