Data-driven distribution network topology identification considering correlated generation power of distributed energy resource

Jialiang CHEN, Xiaoyuan XU, Zheng YAN, Han WANG

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Front. Energy ›› 2022, Vol. 16 ›› Issue (1) : 121-129. DOI: 10.1007/s11708-021-0780-x
RESEARCH ARTICLE
RESEARCH ARTICLE

Data-driven distribution network topology identification considering correlated generation power of distributed energy resource

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Abstract

This paper proposes a data-driven topology identification method for distribution systems with distributed energy resources (DERs). First, a neural network is trained to depict the relationship between nodal power injections and voltage magnitude measurements, and then it is used to generate synthetic measurements under independent nodal power injections, thus eliminating the influence of correlated nodal power injections on topology identification. Second, a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain the network topology by evaluating the dependence among the synthetic measurements. The proposed method is tested on different distribution networks and the simulation results are compared with those of other methods to validate the effectiveness of the proposed method.

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power distribution network / data-driven / topology identification / distributed energy resource / maximal information coefficient

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Jialiang CHEN, Xiaoyuan XU, Zheng YAN, Han WANG. Data-driven distribution network topology identification considering correlated generation power of distributed energy resource. Front. Energy, 2022, 16(1): 121‒129 https://doi.org/10.1007/s11708-021-0780-x

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Acknowledgments

This work was supported by the National Key R&D Program of China (No. 2017YFB0902800) and the National Natural Science Foundation of China (Grant No. 52077136).

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2021 Higher Education Press
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