HIGH VOLTAGE CABLE FAULT IDENTIFICATION METHOD BASED ON BAGGING SAMPLING AND IMPROVED K-NEAREST NEIGHBOR ALGORITHM

Yingchun Xu, Xiangmao Cheng, Peifeng Huang, Runjie Lin, Yangrui Lin, Jia Weng

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References

  1. [1] J. Liu, S. Wang, S. Yan, et al., “Fast detection method onwater tree aging of MV cable based on nonsinusoidal responsemeasurement,” IEEE Transactions on Power Delivery, vol. 38,no. 1, pp. 146–153, 2022.
  2. [2] G. K. Rao and P. Jena, “A novel fault identification and localiza-tion scheme for bipolar DC microgrid,” IEEE Transactions onIndustrial Informatics, vol. 19, no. 12, pp. 11752–11764, 2023.
  3. [3] T. Zhang, S. Dai, and Z. Cai, “Planning and fault control ofurban distribution lines through optimal design,” InternationalJournal of Power and Energy Systems, vol. 45, no. 1, pp. 19–25,2025.
  4. [4] V. A. Lacerda, R. M. Monaro, R. Pe˜na-Alzola, D. Campos-Gaona, and D. V. Coury, “Nonunit distance protection algo-rithm for multiterminal MMC-HVDC systems using DC ca-pacitor resonance frequency,” IEEE Transactions on IndustrialElectronics, vol. 69, no. 12, pp. 12924–12933, 2022.
  5. [5] N. Peng, Y. Li, R. Liang, C. Jiang, H. Xu, W. Jin, Y. Wang,Y. Guan, and R. Yang, “Fault section identification of hybridtransmission lines by the transients in modal domain free from11the refractions and reflections at cross-bonded nodes,” IEEETransactions on Power Delivery, vol. 38, no. 4, pp. 2864–2878,2023.
  6. [6] M. S. Zaky, H. E. Ahmed, M. Elsadd, and M. Elgamasy, “Pro-tection of HVDC transmission systems for integrating renewableenergy resources,” Engineering, Technology & Applied ScienceResearch, vol. 13, no. 6, pp. 12237–12244, 2023.
  7. [7] J. Purohit and R. Dave, “Leveraging deep learning techniques toobtain efficacious segmentation results,” Archives of AdvancedEngineering Science, vol. 1, no. 1, pp. 11–26, 2023.
  8. [8] W. Du, G. Yang, M. Tian, W. Hu, and C. Ma, “Active distri-bution network fault location method based on improved mul-tiverse algorithm,” International Journal of Power and EnergySystems, vol. 45, no. 2, pp. 78–88, 2025.
  9. [9] G. V. Raju and N. V. Srikanth, “A novel protection schemefor transmission lines connected to solar photovoltaic and windturbine farms using fuzzy logic systems and bagged ensemblelearning,” Electrical Engineering, vol. 106, no. 6, pp. 7509–7529,2024.
  10. [10] Solimun and A. A. R. Fernandes, “Ensemble bagging discrim-inant and logistic regression in classification analysis,” NewMathematics and Natural Computation, vol. 21, no. 1, pp. 91–111, 2025.
  11. [11] N. Fassina, F. Ranzato, and M. Zanella, “Robustness verifica-tion of k-nearest neighbors by abstract interpretation,” Knowl-edge and Information Systems, vol. 66, no. 8, pp. 4825–4859,2024.
  12. [12] Z. K. Abdul, A. K. A. Talabani, C. M. Rahman, and S. M.Asaad, “Electrocardiogram heartbeat classification using con-volutional neural network-k nearest neighbor,” ARO-The Sci-entific Journal of Koya University, vol. 12, no. 1, pp. 61–67,2024.
  13. [13] R. Sukshitha, “Empirical likelihood ratio based k-nearest neigh-bours regression,” International Journal of Agricultural & Sta-tistical Sciences, vol. 20, no. 2, pp. 421–428, 2024.
  14. [14] S. Mulewa, A. Parmar, and A. De, “A novel Bagged-CNN ar-chitecture for short-term wind power forecasting,” InternationalJournal of Green Energy, vol. 21, no. 12, pp. 2712–2723, 2024.
  15. [15] Y. Xue, Y. Chang, Y. Zhang, J. Sun, Z. Ji, H. Li, Y. Peng,and J. Zuo, “UAV signal recognition of heterogeneous integratedKNN based on genetic algorithm,” Telecommunication Systems,vol. 85, no. 4, pp. 591–599, 2024.
  16. [16] A. Chanane and H. Houassine, “Toward unique electrical lad-der network model synthesis of a transformer winding high-frequency modeling using K-means and metaheuristic-basedmethod,” COMPEL, vol. 43, no. 1, pp. 247–266, 2024.
  17. [17] S. Mantach, M. Partyka, V. Pevtsov, A. Ashraf, and B. Ko-rdi, “Unsupervised deep learning for detecting number of par-tial discharge sources in stator bars,” IEEE Transactions onDielectrics and Electrical Insulation, vol. 30, no. 6, pp. 2887–2895, 2023.
  18. [18] C. Zhu, H. Yang, X. Jin, K. Xu, and W. Shen, “Locality preserv-ing projections-based spatiotemporal modeling of the tempera-ture distribution of lithium-ion batteries,” IEEE Transactionson Industrial Informatics, vol. 20, no. 1, pp. 179–189, 2023.
  19. [19] B. Despodov, D. Stojanov, and C. M. Bande, “Evaluating hand-written character recognition with Hu moments and k-nearestneighbors algorithm,” TEM Journal, vol. 13, no. 3, p. 1813,2024.
  20. [20] M. Bindi, A. Luchetta, G. M. Lozito, C. F. M. Carobbi,F. Grasso, and M. C. Piccirilli, “Frequency characterization ofmedium voltage cables for fault prevention through multi-valuedneural networks and power line communication technologies,”IEEE Transactions on Power Delivery, vol. 38, no. 5, pp. 3227–3237, 2023.
  21. [21] W. Jiang, D. Wang, B. Liu, Y. Hu, and L. Zhou, “Fault diag-nosis for shielded cable in EMUs based on TLS-ESPRIT and3D-BIS images,” IEEE Transactions on Transportation Elec-trification, vol. 11, no. 1, pp. 2230–2242, 2024.
  22. [22] C. Zhang, M. Chen, Y. Zhang, W. Deng, Y. Gong, andD. Zhang, “Partial discharge pattern recognition algorithm ofoverhead covered conductors based on feature optimization andbidirectional LSTM-GRU,” IET Generation, Transmission &Distribution, vol. 18, no. 4, pp. 680–693, 2024.
  23. [23] Q. Huang, Z. Li, Z. Fu, Y. Hu, Q. Fang, and Y. Wei, “Complexwired network fault diagnosis based on distributed reflectometryand multi-channel 1D-CNN,” IEEE Sensors Journal, vol. 25,no. 11, pp. 19415–19427, 2025.

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