RESEARCH ARTICLE

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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  • Key Laboratory of Control of Power Transmission and Conversion (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 25 Mar 2021

Accepted date: 05 Jun 2021

Published date: 15 Feb 2022

Copyright

2021 Higher Education Press

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.

Cite this article

Jialiang CHEN , Xiaoyuan XU , Zheng YAN , Han WANG . Data-driven distribution network topology identification considering correlated generation power of distributed energy resource[J]. Frontiers in Energy, 2022 , 16(1) : 121 -129 . DOI: 10.1007/s11708-021-0780-x

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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