A METHOD FOR GENERATING AND RECONSTRUCTING POWER GRID SUBSTATION TOPOLOGY BASED ON LSTM LASSO MODEL

Dongxu Zhou, Jingming Zhao, Fanqin Zeng, Xiashan Feng, and Shuoyu Li

References

  1. [1] N. R. Zhou, Y. Zhou, L.H. Gong, and M.L. Jiang, Accurateprediction of photovoltaic power output based on long short-term memory network, IET Optoelectronics, 14(6), 2020,399–405.
  2. [2] F. Li, Q. Li, J. Zhang, J. Kou, J. Ye, W. Song, and H.A.Mantooth, Detection and diagnosis of data integrity attacksin solar farms based on multilayer long short-term memorynetwork, IEEE Transactions on Power Electronics, 36(3),2020, 2495–2498.
  3. [3] S.A. Guti´errez, J.F. Botero, N. G. G´omez, L..A. Fletscher,and A. Leal, Next-generation power substation communicationnetworks: IEC 61850 meets programmable networks, IEEEPower and Energy Magazine, 21(5), 2023, 58–67.
  4. [4] Z. Wang, H. He, Z. Wan, and Y. Sun, Coordinated topologyattacks in smart grid using deep reinforcement learning,IEEE Transactions on Industrial Informatics, 17(2), 2020,1407–1415.
  5. [5] D. Deka, S. Talukdar, M. Chertkov, and M.V. Salapaka,Graphical models in meshed distribution grids: Topologyestimation, change detection & limitations, IEEE Transactionson Smart Grid, 11(5), 2020, 4299–4310.
  6. [6] J. Zhang, Y. Wang, Y. Weng, and N. Zhang, Topologyidentification and line parameter estimation for non-PMUdistribution network: A numerical method, IEEE Transactionson Smart Grid, 11(5), 2020, 4440–4453.
  7. [7] Z. Liu and L. Wang, Leveraging network topology optimizationto strengthen power grid resilience against cyber-physicalattacks, IEEE Transactions on Smart Grid, 12(2), 2020,1552–1564.
  8. [8] B. Li, D. Ofori-Boateng, Y.R. Gel, and J. Zhang, A hybridapproach for transmission grid resilience assessment usingreliability metrics and power system local network topology,Sustainable and Resilient Infrastructure, 6(1–2), 2021, 26–41.
  9. [9] M. Movahednia, A. Kargarian, C.E. Ozdemir, and S.C. Hagen,Power grid resilience enhancement via protecting electricalsubstations against flood hazards: A stochastic framework,IEEE Transactions on Industrial Informatics, 18(3), 2021,2132–2143.
  10. [10] M. Ebadi, M. Bayat, and H. Asadi, Evaluating maximumpermissible feeder current in capacitive compensated harmonicpolluted networks introducing Apparent RMS Current RatioIndex (ACRI), Electric Power Systems Research, 187, 2020,106511.
  11. [11] M. Umer, I. Ashraf, A. Mehmood, S. Kumari, S. Ullah, andG. Sang Choi, Sentiment analysis of tweets using a unifiedconvolutional neural network-long short-term memory networkmodel, Computational Intelligence, 37(1), 2021, 409–434.
  12. [12] S. Punia, K. Nikolopoulos, S.P. Singh, J.K. Madaan, and K.Litsiou, Deep learning with long short-term memory networksand random forests for demand forecasting in multi-channelretail, International Journal of Production Research, 58(16),2020, 4964–4979.
  13. [13] S.K. Pandey and R.R. Janghel. Automatic arrhythmiarecognition from electrocardiogram signals using differentfeature methods with long short-term memory network model,Signal, Image and Video Processing, 14(6), 2020, 1255–1263.
  14. [14] F. Kong, J. Song, and Z. Yang, A novel short-term carbonemission prediction model based on secondary decompositionmethod and long short-term memory network, EnvironmentalScience and Pollution Research, 29(43), 2022, 64983–64998.
  15. [15] M. Yazdi, N.A. Golilarz, A. Nedjati, and K.A. Adesina, Animproved lasso regression model for evaluating the efficiencyof intervention actions in a system reliability analysis, NeuralComputing and Applications, 33(13), 2021,7913–7928.
  16. [16] J K. Sethi and M. Mittal. An efficient correlation based adaptiveLASSO regression method for air quality index prediction,Earth Science Informatics, 14(4), 2021, 1777–1786.
  17. [17] H. Guler and E.O. Guler, Mixed Lasso estimator for stochasticrestricted regression models, Journal of Applied Statistics,48(13–15), 2021, 2795–2808.
  18. [18] X. Ge, F. Xu, Y. Wang, H. Li, F. Wang, J. Hu, and B.Chen, Spatio-temporal two-dimensions data based customerbaseline load estimation approach using LASSO regression,IEEE Transactions on Industry Applications, 58(3), 2022,3112–3122.
  19. [19] G. Cilluffo, G. Sottile, S. La Grutta, and V.M. Muggeo, TheInduced Smoothed lasso: A practical framework for hypothesistesting in high dimensional regression, Statistical Methods inMedical Research, 29(3), 2020, 765–777.
  20. [20] A.R. Inturi, V.M. Manikandan, and V. Garrapally, Anovel vision-based fall detection scheme using keypointsof human skeleton with long short-term memory network,Arabian Journal for Science and Engineering, 48(2), 2023,1143–1155.
  21. [21] H. Cao, Y. Wu, Y. Bao, X. Feng, S. Wan, and C.Qian, UTrans-Net: A model for short-term precipitationprediction, Artificial Intelligence and Applications, 1(2), 2023,106–113.
  22. [22] J. Dai, K. Huang, Y. Liu, C. Yang, and Z. Wang, Globalreconstruction of complex network topology via structuredcompressive sensing, IEEE Systems Journal, 15(2), 2020,1959–1969.9
  23. [23] H. Wu, Z. Xu, J. Zhao, and S. Chai, Gridtopo-GAN fordistribution system topology identification, IEEE Transactionson Industrial Informatics, 19(4), 2022, 5356–5366.
  24. [24] T. Wu, W. Xue, H. Wang, C.Y. Chung, G. Wang, J. Peng, andQ. Yang, Extreme learning machine-based state reconstructionfor automatic attack filtering in cyber physical power system,IEEE Transactions on Industrial Informatics, 17(3), 2020,1892–1904.
  25. [25] G. Yao, Y. Li, Q. Li, S. Hu, and N. Jin, Model predictivepower control for a fault-tolerant grid-connected converterusing reconstructed currents, IET Power Electronics, 13(6),2020, 1181–1190.
  26. [26] T. Kunj, A. Mohan, and K. Pal, Two-way energy managementof electric vehicle charging station, International Journal ofPower and Energy Systems, 44(10), 2024, 1–8.

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