Enhanced DC Microgrid Protection: A 2D Current Modeling and Deep Learning Approach

Articles in Press

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 Electrical and Computer Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran.

3 Faculty of Electrical Engineering, Shahid Beheshti University, Evin, Tehran, Iran.

Abstract

This paper introduces a novel protection method for identifying and locating faults in DC microgrids, which is aimed at overcoming the challenges faced by modern power systems. A two dimensional current modeling technique is utilized to detect faults, in which even minimal changes in the sampled data result in rapid detection due to the model's sensitivity. Additionally, the method differentiates between transient and permanent faults and is robust against noise in sampled signals. Furthermore, a deep learning model based on long short term memory layers, optimized using the whale optimization algorithm, is applied for fault location. The deep learning model's layers are fully aligned with the data, and the optimization process enhances the model's accuracy. The proposed scheme operates without relying on extensive communication links, making it practical for real world applications. Comparative evaluations demonstrate that the system outperforms existing methods in terms of accuracy, speed, and reliability, confirming its effectiveness in DC microgrid protection. The deployment of the proposed method effectively identifies and pinpoints faults at various locations within the microgrid in as little as 1 millisecond and within PV and EV components in up to 11 milliseconds. This capability has been validated across a range of fault types and impedances. Additionally, the method has demonstrated reliable performance despite noisy conditions, maintaining accuracy with a signal to noise ratio of 40 dB.

Keywords

Main Subjects

References

  1. C. Patil, S. Thale, S. Muchande, and A. H. Kadam, “A novel protection scheme for DC microgrid with hierarchical control,” in 2017 IEEE Int. Conf. Smart Energy Grid Eng., pp. 117–122, IEEE, 2017.
  2. R. Mohanty and A. K. Pradhan, “Protection of smart DC microgrid with ring configuration using parameter estimation approach,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6328– 6337, 2017.
  3. R. Dadi, K. Meenakshy, and S. Damodaran, “A review on secondary control methods in DC microgrid,” J. Oper. Autom. Power Eng., vol. 11, no. 2, pp. 105–112, 2023.
  4. E. Naderi, S. Seyedshenava, and H. Shayeghi, “High-gain dc/dc converter implemented with MPPT algorithm for dc microgrid system,” J. Oper. Autom. Power Eng., vol. 11, no. 3, pp. 213–222, 2023.
  5. S. Beheshtaein, R. M. Cuzner, M. Forouzesh, M. Savaghebi, and J. M. Guerrero, “DC microgrid protection: A comprehensive review,” IEEE J. Emerg. Sel. Topics Power Electron., 2019.
  6. D. Marroqui, J. M. Blanes, A. Garrigos, and R. Gutierrez, “Self-powered 380 V DC sic solid-state circuit breaker and fault current limiter,” IEEE Trans. Power Electron., vol. 34, no. 10, pp. 9600–9608, 2019.
  7. L. Chen et al., “Application and design of a resistive-type superconducting fault current limiter for efficient protection of a DC microgrid,” IEEE Trans. Appl. Supercond., vol. 29, no. 2, pp. 1–7, 2018.
  8. R. Eslami and S. A. Hosseini, “A comprehensive method for fault detection in AC/DC hybrid microgrid,” Electr. Power Compon. Syst., vol. 50, no. 1-2, pp. 38–51, 2022.
  9. R. Eslami and S. Hosseini, “Presenting new triple methods for fault detection, location, and its identification in DC microgrid,” Iran. J. Sci. Technol. Trans. Electr. Eng., vol. 44, pp. 849–860, 2020.
  10. D. Salomonsson, L. Soder, and A. Sannino, “Protection of low-voltage dc microgrids,” IEEE Trans. Power Del., vol. 24, no. 3, pp. 1045–1053, 2009.
  11. A. Shabani and K. Mazlumi, “Evaluation of a communication-assisted overcurrent protection scheme for photovoltaic-based dc microgrid,” IEEE Trans. Smart Grid, vol. 11, no. 1, pp. 429–439, 2019.
  12. S. K. Maurya, A. K. Soni, A. Mohapatra, and A. Sharma, “Optimal single settings based relay coordination in DC microgrids for line faults,” Int. J. Electr. Power Energy Syst., vol. 156, p. 109708, 2024.
  13. S.-A. Amamra, H. Ahmed, and R. A. El-Sehiemy, “Firefly algorithm optimized robust protection scheme for DC microgrid,” Electr. Power Compon. Syst., vol. 45, no. 10, pp. 1141–1151, 2017.
  14. S. Augustine, M. J. Reno, S. M. Brahma, and O. Lavrova, “Fault current control and protection in a standalone DC microgrid using adaptive droop and current derivative,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 9, no. 3, pp. 2529–2539, 2020.
  15. E. Christopher, M. Sumner, D. W. Thomas, X. Wang, and F. de Wildt, “Fault location in a zonal DC marine power system using active impedance estimation,” IEEE Trans. Ind. Appl., vol. 49, no. 2, pp. 860–865, 2013.
  16. K. Jia, Q. Zhao, T. Feng, and T. Bi, “Distance protection scheme for DC distribution systems based on the high-frequency characteristics of faults,” IEEE Trans. Power Deliv., vol. 35, no. 1, pp. 234–243, 2019.
  17. R. Montoya, B. P. Poudel, A. Bidram, and M. J. Reno, “Dc microgrid fault detection using multiresolution analysis of traveling waves,” Int. J. Electr. Power Energy Syst., vol. 135, p. 107590, 2022.
  18. K. Saleh, A. Hooshyar, and E. F. El-Saadany, “Fault detection and location in medium-voltage dc microgrids using travelling-wave reflections,” IET Renew. Power Gener., vol. 14, no. 4, pp. 571–579, 2020.
  19. I. Almutairy and M. Alluhaidan, “Fault diagnosis based approach to protecting DC microgrid using machine learning technique,” Procedia Comput. Sci., vol. 114, pp. 449–456, 2017.
  20. P. Pan and R. K. Mandal, “Fault detection and classification in DC microgrid clusters,” Eng. Res. Express, vol. 5, no. 2, p. 025010, 2023.
  21. K. Anjaiah, P. K. Dash, and M. Sahani, “A new protection scheme for pv-wind based dc-ring microgrid by using modified multifractal detrended fluctuation analysis,” Prot. Control Mod. Power Syst., vol. 7, no. 1, p. 8, 2022.
  22. S. Dhar and P. K. Dash, “Differential current-based fault protection with adaptive threshold for multiple PV-based DC microgrid,” IET Renew. Power Gener., vol. 11, no. 6, pp. 778–790, 2017.
  23. M. Li, Y. Luo, K. Jia, T. Bi, and Q. Yang, “Frequency-based current differential protection for VSC-MVDC distribution lines,” Int. J. Electr. Power Energy Syst., vol. 117, p. 105626, 2020.
  24. W. Zhang, H. Zhang, and N. Zhi, “A novel protection strategy for DC microgrid considering communication failure,” Energy Rep., vol. 9, pp. 2035–2044, 2023.
  25. R. Rahmani, S. H. H. Sadeghi, H. Askarian-Abyaneh, and M. J. Emadi, “An entropy-based scheme for protection of dc microgrids,” Electr. Power Syst. Res., vol. 228, p. 110010, 2024.
  26. Z. Moravej, A. Ebrahimi, and M. Barati, “A new differential protection scheme based on synchro-squeezing transform applied to ac micro-grid,” Electr. Power Syst. Res., vol. 231, p. 110336, 2024.
  27. K. Ding et al., “Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion,” Appl. Energy, vol. 353, p. 122135, 2024.
  28. A. Amiri, H. Samet, and T. Ghanbari, “Recurrence plots based method for detecting series arc faults in photovoltaic systems,” IEEE Trans. Ind. Electron., vol. 69, no. 6, pp. 6308–6315, 2021.
  29. M. Xu, L. Xiao, and H. Wang, “A prony-based method of locating short-circuit fault in dc distribution system,” 2013.
  30. D. Jayamaha, N. Lidula, and A. Rajapakse, “Protection and grounding methods in dc microgrids: Comprehensive review and analysis,” Renew. Sust. Energ. Rev., vol. 120, p. 109631, 2020.
  31. R. Mohanty, U. S. M. Balaji, and A. K. Pradhan, “An accurate noniterative fault-location technique for low-voltage dc microgrid,” IEEE Trans. Power Deliv., vol. 31, no. 2, pp. 475–481, 2015.
  32. A. Meghwani, S. C. Srivastava, and S. Chakrabarti, “Local measurement-based technique for estimating fault location in multi-source dc microgrids,” IET Gener. Transm. Distrib., vol. 12, no. 13, pp. 3305–3313, 2018.
  33. Y. Yang, C. Huang, D. Zhou, and Y. Li, “Fault detection and location in multi-terminal dc microgrid based on local measurement,” Electr. Power Syst. Res., vol. 194, p. 107047, 2021.
  34. M. A. Aftab, S. S. Hussain, I. Ali, and T. S. Ustun, “Dynamic protection of power systems with high penetration of renewables: A review of the traveling wave based fault location techniques,” Int. J. Electr. Power Energy Syst., vol. 114, p. 105410, 2020.
  35. S. Dhar, R. K. Patnaik, and P. Dash, “Fault detection and location of photovoltaic based dc microgrid using differential protection strategy,” IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 4303–4312, 2017.
  36. Q. Yang, J. Li, S. Le Blond, and C. Wang, “Artificial neural network based fault detection and fault location in the dc microgrid,” Energy Procedia, vol. 103, pp. 129–134, 2016.
  37. S. Salehimehr, S. M. Miraftabzadeh, and M. Brenna, “A novel machine learning-based approach for fault detection and location in low-voltage dc microgrids,” Sustainability, vol. 16, no. 7, p. 2821, 2024.
  38. C. Li, P. Rakhra, P. J. Norman, G. M. Burt, and P. Clarkson, “Multi-sample differential protection scheme in dc microgrids,” IEEE J. Emerg. Sel. Top. Power Electron., vol. 9, no. 3, pp. 2560–2573, 2020.
  39. S. Z. Jamali et al., “A high-speed fault detection, identification, and isolation method for a last mile radial lvdc distribution network,” Energies, vol. 11, no. 11, p. 2901, 2018.
  40. L. Zhang, N. Tai, W. Huang, J. Liu, and Y. Wang, “A review on protection of dc microgrids,” J. Mod. Power Syst. Clean Energy, vol. 6, no. 6, pp. 1113–1127, 2018.
  41. S. Wang, T. Bi, and K. Jia, “Wavelet entropy based fault detection approach for mmc-hvdc lines,” in 2015 IEEE Power Energy Society General Meeting, pp. 1–5, IEEE, 2015.
  42. M. Shaban, M. A. Mosa, A. Ali, and K. Abdel-Latif, “Effect of power sharing control techniques of hybrid energy storage system during fault conditions in dc microgrid,” J. Energy Storage, vol. 72, p. 108249, 2023.
  43. L. Shen, Q. Cheng, Y. Cheng, L. Wei, and Y. Wang, “Hierarchical control of dc micro-grid for photovoltaic ev charging station based on flywheel and battery energy storage system,” Electr. Power Syst. Res., vol. 179, p. 106079, 2020.
  44. B. Taheri and A. Shahhoseini, “Direct current (dc) microgrid control in the presence of electrical vehicle/photovoltaic (ev/pv) systems and hybrid energy storage systems: A case study of grounding and protection issue,” IET Gener. Transm. Distrib., 2023.
  45. M. T. Sadiq, X. Yu, Z. Yuan, and M. Z. Aziz, “Motor imagery bci classification based on novel two-dimensional modelling in empirical wavelet transform,” Electron. Lett., vol. 56, no. 25, pp. 1367–1369, 2020.
  46. S. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, “Gradient flow in recurrent nets: the difficulty of learning long-term dependencies,” in A Field Guide to Dynamical Recurrent Neural Networks, IEEE Press, 2001.
  47. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.
  48. K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “Lstm: A search space odyssey,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 10, pp. 2222–2232, 2016.
  49. C. Zhang et al., “Multi-imbalance: An open-source software for multi-class imbalance learning,” Knowl.-Based Syst., vol. 174, pp. 137–143, 2019.
  50. F. Huang, X. Zhang, Z. Li, Z. Zhao, and Y. He, “From content to links: Social image embedding with deep multimodal model,” Knowl.-Based Syst., vol. 160, pp. 251–264, 2018.
  51. Y. Wu, H. Mao, and Z. Yi, “Audio classification using attention-augmented convolutional neural network,” Knowl.Based Syst., vol. 161, pp. 90–100, 2018.
  52. C. Zhang, C. Liu, X. Zhang, and G. Almpanidis, “An up-todate comparison of state-of-the-art classification algorithms,” Expert Syst. Appl., vol. 82, pp. 128–150, 2017.
  53. S. Salehimehr, B. Taheri, and M. Sedighizadeh, “Short-term load forecasting in smart grids using artificial intelligence methods: A survey,” J. Eng., vol. 2022, no. 12, pp. 1133–1142, 2022.
  54. B.-S. Kwon, R.-J. Park, and K.-B. Song, “Short-term load forecasting based on deep neural networks using lstm layer,” J. Electr. Eng. Technol., vol. 15, pp. 1501–1509, 2020.
  55. P. Wang, J. Zhao, Y. Gao, M. A. Sotelo, and Z. Li, “Lane work-schedule of toll station based on queuing theory and pso-lstm model,” IEEE Access, vol. 8, pp. 84434–84443, 2020.
  56. X. Ren, S. Liu, X. Yu, and X. Dong, “A method for state-of-charge estimation of lithium-ion batteries based on pso-lstm,” Energy, vol. 234, p. 121236, 2021.
  57. B. Shao, M. Li, Y. Zhao, and G. Bian, “Nickel price forecast based on the lstm neural network optimized by the improved pso algorithm,” Math. Probl. Eng., vol. 2019, 2019.
  58. N. Xu, X. Wang, X. Meng, and H. Chang, “Gas concentration prediction based on iwoa-lstm-ceemdan residual correction model,” Sensors, vol. 22, no. 12, p. 4412, 2022.
  59. S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Adv. Eng. Softw., vol. 95, pp. 51–67, 2016.
  60. L. Shao, Q. Guo, C. Li, J. Li, and H. Yan, “Short-term load forecasting based on eemd-woa-lstm combination model,” Appl. Bionics Biomech., vol. 2022, 2022.
  61. N. J. Marand, H. S. Daliri, and H. Askarian-Abyaneh, “Removal of dc offset components by using two auxiliary signals,” in 2023 IEEE PES GTD Int. Conf. Expo., pp. 226– 230, IEEE, 2023.
  62. M. Dashtdar, O. Rubanenko, D. Danylchenko, S. M. S. Hosseinimoghadam, N. K. Sharma, and M. Baiai, “Protection of dc microgrids based on differential protection method by fuzzy systems,” in 2021 IEEE 2nd KhPI Week Adv. Technol., pp. 22–27, IEEE, 2021.
  63. B. Taheri, S. A. Hosseini, S. Salehimehr, and F. Razavi, “A novel approach for detection high impedance fault in dc microgrid,” in 2019 Int. Power Syst. Conf., pp. 287–292, IEEE, 2019.
  64. G. Li et al., “Analysis and fault location of single-pole ground fault in lvdc ungrounded system,” in 4th Int. Conf. Mech., Electron., Electr. Autom. Control, vol. 13163, pp. 953–959, SPIE, 2024.
  65. P. Pan and R. K. Mandal, “Intelligent short-circuit protection with solid-state circuit breakers for low-voltage dc microgrids,” IETE J. Res., pp. 1–17, 2023.
  66. C. Zhang, H. Wang, Z. Wang, and Y. Li, “Active detection fault diagnosis and fault location technology for lvdc distribution networks,” Int. J. Electr. Power Energy Syst., vol. 148, p. 108921, 2023.
  67. S. Sanati, M. Azzouz, and A. Awad, “An adaptive overcurrent protection for solar-based dc microgrids using iec 61850,” arXiv preprint arXiv:2307.01940, 2023.
  68. M. Mola, A. Afshar, N. Meskin, and M. Karrari, “Distributed fast fault detection in dc microgrids,” IEEE Syst. J., vol. 16, no. 1, pp. 440–451, 2020.
  69. N. K. Sharma, S. R. Samantaray, and C. N. Bhende, “Vmd-enabled current-based fast fault detection scheme for dc microgrid,” IEEE Syst. J., vol. 16, no. 1, pp. 933–944, 2021.
  70. Y. R. O, S. Chatterjee, A. K. Chakraborty, and A. R. Bhowmik, “Fault detection and location estimation for lvdc microgrid using self-parametric measurements,” Int. Trans. Electr. Energy Syst., vol. 30, no. 9, p. e12499, 2020.
  71. L. Ortiz, J. W. González, L. B. Gutierrez, and O. LlanesSantiago, “A review on control and fault-tolerant control systems of ac/dc microgrids,” Heliyon, vol. 6, no. 8, 2020.

 

Journal of Operation and Automation in Power Engineering

Articles in Press, Corrected Proof
Available Online from 27 November 2025

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  • Receive Date: 18 September 2024
  • Revise Date: 25 December 2024
  • Accept Date: 06 February 2025
  • First Publish Date: 27 November 2025

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