Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Frontiers in Energy Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jiang H, Zhang J J, Gao W, et al. Fault detection, identification, and location in smart grid based on data-driven computational methods. IEEE Transactions on Smart Grid, 2014, 5(6): 2947–2956

    Article  Google Scholar 

  2. Pignati M, Zanni L, Romano P, et al. Fault detection and faulted line identification in active distribution networks using synchrophasors-based real-time state estimation. IEEE Transactions on Power Delivery, 2017, 32(1): 381–392

    Article  Google Scholar 

  3. Erdiwansyah M, Mahidin, Husin H, et al. A critical review of the integration of renewable energy sources with various technologies. Protection and Control of Modern Power Systems, 2021, 6 (1): 3

    Article  Google Scholar 

  4. Irving M R, Sterling M J H. Substation data validation. IEE Proceedings C (Generation, Transmission and Distribution), 1982, 129(3): 119–122

    Article  Google Scholar 

  5. Zhu H, Giannakis G B. Sparse overcomplete representations for efficient identification of power line outages. IEEE Transactions on Power Systems, 2012, 27(4): 2215–2224

    Article  Google Scholar 

  6. He M, Zhang J. A dependency graph approach for fault detection and localization towards secure smart grid. IEEE Transactions on Smart Grid, 2011, 2(2): 342–351

    Article  Google Scholar 

  7. Zhou D, Ma S, Huang D, et al. An operating state estimation model for integrated energy systems based on distributed solution. Frontiers in Energy, 2020, 14(4): 801–816

    Article  Google Scholar 

  8. Sharma R, Suhag S. Feedback linearization based control for weak grid connected PV system under normal and abnormal conditions. Frontiers in Energy, 2020, 14(2): 400–409

    Article  Google Scholar 

  9. Zhang N, Sun Q, Wang J, et al. Distributed adaptive dual control via consensus algorithm in the energy Internet. IEEE Transactions on Industrial Informatics, 2021, 17(7): 4848–4860

    Article  Google Scholar 

  10. Zhang N, Sun Q, Yang L, et al. Event-triggered distributed hybrid control scheme for the integrated energy system. IEEE Transactions on Industrial Informatics, 2021, online, doi:https://doi.org/10.1109/TII.2021.3075718

  11. Li R, Wong P, Wang K, et al. Power quality enhancement and engineering application with high permeability distributed photovoltaic access to low-voltage distribution networks in Australia. Protection and Control of Modern Power Systems, 2020, 5(1): 18

    Article  Google Scholar 

  12. Alam S M S, Natarajan B, Pahwa A. Distribution grid state estimation from compressed measurements. IEEE Transactions on Smart Grid, 2014, 5(4): 1631–1642

    Article  Google Scholar 

  13. Cavraro G, Kekatos V, Veeramachaneni S. Voltage analytics for power distribution network topology verification. IEEE Transactions on Smart Grid, 2019, 10(1): 1058–1067

    Article  Google Scholar 

  14. Tian Z, Wu W, Zhang B. A mixed integer quadratic programming model for topology identification in distribution network. IEEE Transactions on Power Systems, 2016, 31(1): 823–824

    Article  Google Scholar 

  15. Cunha V C, Freitas W, Trindade F C L, et al. Automated determination of topology and line parameters in low voltage systems using smart meters measurements. IEEE Transactions on Smart Grid, 2020, 11(6): 5028–5038

    Article  Google Scholar 

  16. Jiang W, Chen J, Tang H, et al. A physical probabilistic network model for distribution network topology recognition using smart meter data. IEEE Transactions on Smart Grid, 2019, 10(6): 6965–6973

    Article  Google Scholar 

  17. Weng Y, Liao Y, Rajagopal R. Distributed energy resources topology identification via graphical modeling. IEEE Transactions on Power Systems, 2017, 32(4): 2682–2694

    Article  Google Scholar 

  18. Pappu S J, Bhatt N, Pasumarthy R, et al. Identifying topology of low voltage distribution networks based on smart meter data. IEEE Transactions on Smart Grid, 2018, 9(5): 5113–5122

    Article  Google Scholar 

  19. Deka D, Backhaus S, Chertkov M. Structure learning in power distribution networks. IEEE Transactions on Control of Network Systems, 2018, 5(3): 1061–1074

    Article  MathSciNet  MATH  Google Scholar 

  20. Cover T M, Thomas J A. Elements of Information Theory. New York: John Wiley & Sons, Inc., 1991

    Book  MATH  Google Scholar 

  21. Chow C, Liu C. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 1968, 14(3): 462–467

    Article  MATH  Google Scholar 

  22. Reshef D N, Reshef Y A, Finucane H K, et al. Detecting novel associations in large data sets. Science, 2011, 334(6062): 1518–1524

    Article  MATH  Google Scholar 

  23. Reshef Y A, Reshef D N, Finucane H K, et al. Measuring dependence powerfully and equitably. Journal of Machine Learning Research, 2016, 17(1): 7406–7468

    MathSciNet  MATH  Google Scholar 

  24. Roberts M B, Haghdadi N, Bruce A, et al. Clustered residential electricity load profiles from smart grid smart city dataset. 2019, available at the website of narcis.nl

  25. Zimmerman R D, Murillo-Sanchez C E, Thomas R J. MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems, 2011, 26(1): 12–19

    Article  Google Scholar 

  26. Albanese D, Filosi M, Visintainer R, et al. Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. Bioinformatics (Oxford, England), 2013, 29(3): 407–408

    Google Scholar 

  27. UK Power Networks. Photovoltaic (PV) solar panel energy generation data. London Datastore, 2020-08-18, available at the website of data.london.gov.uk

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

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

    Jialiang Chen, Xiaoyuan Xu, Zheng Yan & Han Wang

Authors
  1. Jialiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyuan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zheng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyuan Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Xu, X., Yan, Z. et al. Data-driven distribution network topology identification considering correlated generation power of distributed energy resource. Front. Energy 16, 121–129 (2022). https://doi.org/10.1007/s11708-021-0780-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11708-021-0780-x

Keywords

Search

Navigation