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

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.

Graphical abstract

Keywords

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

References

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

[2] Pignati M, Zanni L, Romano P, 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

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

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

[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

[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

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

[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

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

[10] Zhang N, Sun Q, Yang L, Event-triggered distributed hybrid control scheme for the integrated energy system. IEEE Transactions on Industrial Informatics, 2021, online,

[11] Li R, Wong P, Wang K, 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

[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

[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

[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

[15] Cunha V C, Freitas W, Trindade F C L, 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

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

[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

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

[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

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

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

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

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

[24] Roberts M B, Haghdadi N, Bruce A, 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

[26] Albanese D, Filosi M, Visintainer R, 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

[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

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).

RIGHTS & PERMISSIONS

2021 Higher Education Press