Machine Learning-based Fault Detection and Classification in microgrid

Document Type : Research paper

Authors

1 Vice-Rector for Scientific Affairs and Innovation, International School of Finance Technology and Science, Uzbekistan.

2 Tashkent State University of Economics, Tashkent, Uzbekistan

3 Department of "Digital Economy", Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan.

4 Kimyo International University in Tashkent , Shota Rustaveli Street 156, 100121, Тashkent, Uzbekistan.

5 Department of Network Economics, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan.

6 Department Physics and Chemistry, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Tashkent, Uzbekistan.

7 Scientific University of Tashkent for Applied Sciences, Street Gavhar 1, Tashkent 100149, Uzbekistan.

8 Department of Digital Economy, Samarkand State University Named after Sharof Rashidov, University Boulevard, 15, Samarkand, 703004, Uzbekistan.

9 Department of General Sciences and Culture, Tashkent State University of Law, Uzbekistan.

10 Urganch State University, Uzbekistan.

11 Termez State University, Termez, Uzbekistan.

12 Department of Fundamental Economic Science of the International School of Finance Technology and Science, Uzbekistan.

10.22098/joape.2025.16912.2315

Abstract

Fault Detection and Classification plays a vital role in maintaining the reliability and stability of microgrids, especially as they incorporate renewable energy sources and become more decentralized. Microgrids face a wide variety of faults, such as short circuits, line-to-ground faults, and other disturbances, which can negatively affect system performance. Traditional fault detection methods have primarily focused on False Data Injection and cyber-attacks, emphasizing vulnerabilities in communication infrastructure. However, this study addresses current faults within the electrical network, focusing on system stability and real-time fault detection in the absence of communication-related errors. In this work, machine learning techniques are employed to enhance fault classification accuracy. Partial Least Squares is used for feature selection to extract relevant statistical features from real-time current data collected from various microgrid components. By optimizing these features and applying them to machine learning models, the approach overcomes the limitations of conventional fault detection methods. The results show a significant improvement in fault classification performance, with up to 10% higher accuracy compared to traditional methods. Additionally, the use of data from neighboring microgrid components boosts the model's robustness, adaptability, and performance under varying operational conditions, contributing to a more resilient microgrid. This research introduces an innovative approach to fault detection in microgrids by combining machine learning and feature optimization, offering a more accurate, reliable, and efficient solution to ensure continuous energy supply and improve system stability under different fault scenarios.

Keywords

Main Subjects

References

  1. M. Mishra and P. K. Rout, “Detection and classification of micro-grid faults based on hht and machine learning techniques,” IET Gener. Transm. Distrib., vol. 12, no. 2, pp. 388–397, 2018.
  2. B. Roy, S. Adhikari, S. Datta, K. J. Devi, A. D. Devi, F. Alsaif, and T. S. Ustun, “Deep learning based relay for online fault detection, classification, and fault location in a grid-connected microgrid,” IEEE Access, vol. 11, pp. 62674–62696, 2023.
  3. 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.
  4. M. Zaben, M. Y. Worku, M. A. Hassan, and M. A. Abido, “Machine learning methods for fault diagnosis in ac microgrids: A systematic review,” IEEE Access, 2024.
  5. A. Younesi, H. Shayeghi, and P. Siano, “Assessing the use of reinforcement learning for integrated voltage/frequency control in ac microgrids,” Energies, vol. 13, no. 5, p. 1250, 2020.
  6. M. Uzair, M. Eskandari, L. Li, and J. Zhu, “Machine learning based protection scheme for low voltage ac microgrids,” Energies, vol. 15, no. 24, p. 9397, 2022.
  7. A. Y. Dewi, M. Y. Arabi, Z. F. Al-Lami, M. M. Abdulhasan, A. S. Ibrahim, R. Sattar, and Y. Yerkin, “Enhancing smart grid systems: A novel mathematical approach for optimized fault recovery and improved energy efficiency,” J. Oper. Autom. Power Eng., vol. 11, no. Special Issue, 2023.
  8. S. Baloch and M. S. Muhammad, “An intelligent data mining-based fault detection and classification strategy for microgrid,” IEEE Access, vol. 9, pp. 22470–22479, 2021.
  9. B. K. Aryan, O. Sobhana, G. C. Prabhakar, and N. A. Reddy, “Fault detection and classification in micro grid using ai technique,” in Proc. Int. Conf. Recent Trends Microelectron. Autom. Comput. Commun. Syst., pp. 1–6, 2022.
  10. S. N. V. B. Rao, Y. P. Kumar, M. Amir, and S. M. Muyeen, “Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks,” Electr. Eng., pp. 1–18, 2024.
  11. Z. Ali, Y. Terriche, M. Jahannoush, S. Z. Abbas, and C. L. Su, “Fault detection and classification in hybrid shipboard microgrids,” in Proc. IEEE PES 14th Asia-Pac. Power Energy Eng. Conf., pp. 1–6, 2022.
  12. B. L. Nguyen, T. Vu, T. T. Nguyen, M. Panwar, and R. Hovsapian, “1-d convolutional graph convolutional networks for fault detection in distributed energy systems,” in Proc. IEEE Ind. Electron. Soc. Annu. On-Line Conf., pp. 1–6, 2022.
  13. Azizi and S. Seker, “Microgrid fault detection and classification based on the boosting ensemble method with the hilbert-huang transform,” IEEE Trans. Power Del., vol. 37, no. 3, pp. 2289–2300, 2021.
  14. M. Zhou and S. Liu, “A hybrid framework combining data-driven anomaly detection and physics-based methods for cyberattack resilience in smart grids,” IEEE Trans. Smart Grid, vol. 12, no. 4, pp. 3205–3215, 2021.
  15. O. Y. Reddy, S. Chatterjee, and A. K. Chakraborty, “Bilayered fault detection and classification scheme for low-voltage dc microgrid with weighted knn and decision tree,” Int. J. Green Energy, vol. 19, no. 11, pp. 1149–1159, 2022.
  16. M. AlSaba and M. Abido, “An efficient machine learning model for microgrid fault detection and classification: Protection approach,” in Proc. IEEE Power Energy Soc. Gen. Meet., pp. 1–5, 2023.
  17. S. R. Fahim, K. S. Sarker, S. M. Muyeen, M. R. I. Sheikh, and S. K. Das, “Microgrid fault detection and classification: Machine learning based approach, comparison, and reviews,” Energies, vol. 13, no. 13, p. 3460, 2020.
  18. Y. Zhang, X. Wang, and L. Chen, “A secure and efficient authentication scheme for smart grid communications with focus on vehicle charging and smart city services,” IEEE Trans. Smart Grid, vol. 14, no. 3, pp. 1590–1602, 2023.
  19. C. Cepeda, C. Orozco-Henao, W. Percybrooks, J. D. PulgarínRivera, O. D. Montoya, W. Gil-González, and J. C. Vélez, “Intelligent fault detection system for microgrids,” Energies, vol. 13, no. 5, p. 1223, 2020.
  20. M. M. Hossain and S. R. Kolla, “Fault detection and classification in microgrid using long short-term memory,” in Proc. IEEE Int. Conf. Electro Inf. Technol., pp. 019–026, 2022.
  21. S. K. Barik, “Fault detection and classification of dc microgrid based on vmd,” COMPEL Int. J. Comput. Math. Electr. Electron. Eng., vol. 42, no. 2, pp. 302–322, 2023.
  22. C. Srivastava, M. Tripathy, and L. Wang, “Fault detection and classification of dc microgrid utilizing differential protection scheme,” in Proc. IEEE IAS Glob. Conf. Emerg. Technol., pp. 96–101, 2022.
  23. M. Barkhi, J. Poorhossein, and S. A. Hosseini, “Integrating fault detection and classification in microgrids using supervised machine learning considering fault resistance uncertainty,” Sci. Rep., vol. 14, no. 1, p. 28466, 2024.
  24. 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.
  25. S. Banerjee and P. S. Bhowmik, “Machine learning based classifiers for dynamic and transient disturbance classification in smart microgrid system,” Meas., vol. 240, p. 115576, 2025.
  26. B. Taheri, S. A. Hosseini, and H. Hashemi-Dezaki, “Enhanced fault detection and classification in ac microgrids through a combination of data processing techniques and deep neural networks,” Sustainability, vol. 17, no. 4, p. 1514, 2025.
  27. A. Kurmaiah and C. Vaithilingam, “Optimization of fault identification and location using adaptive neuro-fuzzy inference system and support vector machine for an ac microgrid,” IEEE Access, 2025.
  28. A. Hussain and G. Ravikumar, “Deep learning-based autoencoder for fault detection and localization in der microgrids,” in 2025 IEEE PES Grid Edge Technol. Conf. Exposition, pp. 1–5, IEEE, 2025.
  29. B. Everett, An introduction to latent variable models. Springer Science & Business Media, 2013.
  30. J. A. Rad, S. Chakraverty, and K. Parand, Dimensionality Reduction in Machine Learning. Morgan Kaufmann, 2025.
  31. R. Raut and S. Dudul, “Design and performance analysis of mlp nn based binary classifier for heart diseases,” Indian J. Sci. Technol., vol. 2, no. 8, pp. 43–48, 2009.
  32. Y. Zhang et al., “Federated deep reinforcement learning for varying-scale multi-energy microgrids energy management considering comprehensive security,” Appl. Energy, vol. 380, p. 125072, 2025.
  33. N. Goel and N. K. Yadav, “Sustainable microgrid operations: multi-objective hybrid optimization for combined economic emission dispatch,” Electr. Eng., pp. 1-14, 2025.
  34. R. Aazami et al., “Deep neural networks based method to islanding detection for multi-sources microgrid,” Energy Rep., vol. 11, pp. 2971–2982, 2024.
  35. R. Kumari and B. K. Naick, “Enhancing protection in AC microgrids: An adaptive approach with ANN and ANFIS models,” Comput. Electr. Eng., vol. 115, p. 109103, 2024.
  36. A. M. Abd-Rabou, A. M. Soliman, and A. S. Mokhtar, “Impact of dg different types on the grid performance,” J. Electr. Syst. Inf. Technol., vol. 2, no. 2, pp. 149–160, 2015.
Journal of Operation and Automation in Power Engineering
Volume 12, Special Issue (Open)
Advanced Technologies for Resilient and Efficient Microgrid Management: Innovations in Energy Optimization, Security, and Integration
2024
Pages 43-52

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History

  • Receive Date: 04 March 2025
  • Revise Date: 08 April 2025
  • Accept Date: 11 April 2025
  • First Publish Date: 11 April 2025

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