Enhancing Microgrid Resilience with LSTM and Fuzzy Logic for Predictive Maintenance

Document Type : Research paper

Authors

Power supply and electric drive department, Almaty University of Power Engineering and Telecommunications, Almaty 050013, Kazakhstan;

Abstract

Microgrids have become integral to modern energy systems, providing decentralized and resilient energy solutions. However, ensuring the reliability of microgrid assets poses significant challenges, particularly given aging infrastructure and unpredictable environmental conditions. While existing methods—such as predictive maintenance, real-time monitoring, and fault detection utilizing Support Vector Machines (SVM), Random Forests, and Principal Component Analysis (PCA)—enhance reliability, they often fall short due to insufficient multidimensional data analysis and limited support for realistic decision-making. This underscores the need for advanced approaches in microgrid management. In this paper, we propose an innovative machine learning-based methodology that integrates Long Short-Term Memory (LSTM) networks with fuzzy logic for predictive maintenance of microgrid assets. The proposed approach effectively addresses the inherent fluctuations and dynamic behavior of microgrids, enhancing system resilience and reducing downtime. By leveraging LSTM's ability to capture temporal patterns alongside fuzzy logic's capacity for handling uncertainties, the method proactively identifies and mitigates potential equipment failures. Traditional maintenance strategies predominantly rely on reactive mechanisms, resulting in higher costs and increased system vulnerabilities. Simulation results indicate that the proposed algorithm achieves a 10% to 40% improvement in fault detection across varying failure levels, demonstrating significant advantages over conventional techniques.

Keywords

Main Subjects

References

  1. M. H. Saeed, W. Fangzong, B. A. Kalwar, and S. Iqbal, “A review on microgrids’ challenges & perspectives,” IEEE Access, vol. 9, pp. 166502–166517, 2021.
  2. G. Shahgholian, “A brief review on microgrids: Operation, applications, modeling, and control,” Int. Trans. Electr. Energy Syst., vol. 31, no. 6, p. e12885, 2021.
  3. K. Keisang, T. Bader, and R. Samikannu, “Review of operation and maintenance methodologies for solar photovoltaic microgrids,” Front. Energy Res., vol. 9, p. 730230, 2021.
  4. J. Hu, Y. Shan, J. M. Guerrero, A. Ioinovici, K. W. Chan, and J. Rodriguez, “Model predictive control of microgrids–an overview,” Renewable Sustainable Energy Rev., vol. 136, p. 110422, 2021.
  5. M. Arafat, M. Hossain, and M. M. Alam, “Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects,” Renewable Sustainable Energy Rev., vol. 190, p. 114088, 2024.
  6. F. M. Almasoudi, “Enhancing power grid resilience through real-time fault detection and remediation using advanced hybrid machine learning models,” Sustainability, vol. 15, no. 10, p. 8348, 2023.
  7. P. Solainayagi, “Decentralized energy using iot and cloud computing for enhancing grid resilience through microgrid integration,” in 2024 6th Int. Conf. Energy Power Environ., pp. 1–6, IEEE, 2024.
  8. M. Elsisi, C.-L. Su, C.-H. Lin, and T.-T. Ku, “Enhancing resilient operation of distributed energy resources using reliable machine learning-based iot connectivity,” in 2024 IEEE/IAS 60th Ind. Commer. Power Syst. Tech. Conf., pp. 1–6, IEEE, 2024.
  9. K. Gokulraj and C. Venkatramanan, “Advanced machine learning-driven security and anomaly identification in inverterbased cyber-physical microgrids,” Electr. Power Compon. Syst., pp. 1–18, 2024.
  10. U. AlHaddad, A. Basuhail, M. Khemakhem, F. E. Eassa, and K. Jambi, “Towards sustainable energy grids: A machine learning-based ensemble methods approach for outages estimation in extreme weather events,” Sustainability, vol. 15, no. 16, p. 12622, 2023.
  11. J. Wang, P. Pinson, S. Chatzivasileiadis, M. Panteli, G. Strbac, and V. Terzija, “On machine learning-based techniques for future sustainable and resilient energy systems,” IEEE Trans. Sustainable Energy, vol. 14, no. 2, pp. 1230–1243, 2022.
  12. H. Shayeghi and E. Shahryari, “Optimal operation management of grid-connected microgrid using multiobjective group search optimization algorithm,” J. Oper. Autom. Power Eng., vol. 5, no. 2, pp. 227–239, 2017.
  13. A. Dizaji, M. Saniei, and K. Zare, “Resilient operation scheduling of microgrid using stochastic programming considering demand response and electric vehicles,” J. Oper. Autom. Power Eng., vol. 7, pp. 157–67, 2019.
  14. K. Masoudi and H. Abdi, “Multi-objective stochastic programming in microgrids considering environmental emissions,” J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 141–151, 2020.
  15. L. Duchesne, E. Karangelos, and L. Wehenkel, “Recent developments in machine learning for energy systems reliability management,” Proc. IEEE, vol. 108, no. 9, pp. 1656–1676, 2020.
  16. F. Aminifar, M. Abedini, T. Amraee, P. Jafarian, M. H. Samimi, and M. Shahidehpour, “A review of power system protection and asset management with machine learning techniques,” Energy Syst., vol. 13, no. 4, pp. 855–892, 2022.
  17. Y. Fassi, V. Heiries, J. Boutet, and S. Boisseau, “Towards physics-informed machine learning-based predictive maintenance for power converters–a review,” IEEE Trans. Power Electron., 2023.
  18. M. S. Qureshi, S. Umar, and M. U. Nawaz, “Machine learning for predictive maintenance in solar farms,” Int. J. Adv. Eng. Technol. Innovations, vol. 1, no. 3, pp. 27–49, 2024.
  19. M. A. Mahmoud, N. R. Md Nasir, M. Gurunathan, P. Raj, and S. A. Mostafa, “The current state of the art in research on predictive maintenance in smart grid distribution network: Fault’s types, causes, and prediction methods—a systematic review,” Energies, vol. 14, no. 16, p. 5078, 2021.
  20. R. K. Kaushal, K. Raveendra, N. Nagabhooshanam, M. Azam, G. Brindha, D. Anand, L. Natrayan, and K. Rambabu, “Fault prediction and awareness for power distribution in grid connected res using hybrid machine learning,” Electr. Power Compon. Syst., pp. 1–22, 2024.
  21. A. T. Eseye, X. Zhang, B. Knueven, and W. Jones, “Enhancing distribution grid resilience through model predictive controller enabled prioritized load restoration strategy,” in 2020 52nd N. Am. Power Symp., pp. 1–6, IEEE, 2021.
  22. J.-F. Toubeau, L. Pardoen, L. Hubert, N. Marenne, J. Sprooten, Z. De Grève, and F. Vallée, “Machine learning-assisted outage planning for maintenance activities in power systems with renewables,” Energy, vol. 238, p. 121993, 2022.
  23. J. Wang, P. Pinson, S. Chatzivasileiadis, M. Panteli, G. Strbac, and V. Terzija, “On machine learning-based techniques for future sustainable and resilient energy systems,” IEEE Trans. Sustainable Energy, vol. 14, no. 2, pp. 1230–1243, 2022.
  24. [24]    H. Noorazar, A. Srivastava, S. Pannala, and S. K Sadanandan, “Data-driven operation of the resilient electric grid: A case of covid-19,” J. Eng., vol. 2021, no. 11, pp. 665–684, 2021.
  25. E.    Ghayoula,    J.    Fattahi,    R.    Ghayoula,    E.    Pricop, G. Stamatescu, J.-Y. Chouinard, and A. Bouallegue, “Sidelobe level reduction in linear array pattern synthesis using taylormusic algorithm for reliable ieee 802.11 mimo applications,” in 2016 IEEE Int. Conf. Syst. Man Cybern., pp. 004700– 004705, IEEE, 2016.
  26. K. Gokulraj and C. Venkatramanan, “Advanced machine learning-driven security and anomaly identification in inverterbased cyber-physical microgrids,” Electr. Power Compon. Syst., pp. 1–18, 2024.
  27. A. Hamdan, K. I. Ibekwe, V. I. Ilojianya, S. Sonko, E. A. Etukudoh, et al., “Ai in renewable energy: A review of predictive maintenance and energy optimization,” Int. J. Sci. Res. Arch., vol. 11, no. 1, pp. 718–729, 2024.
  28. O. Koduri, R. Ramachandran, and M. Saiveerraju, “Empirical mode decomposition and optimization assisted ann based fault classification schemes for series capacitor compensated transmission line,” J. Oper. Autom. Power Eng., vol. 13, no. 1, pp. 52–73, 2025.
  29. F. M. Almasoudi, “Enhancing power grid resilience through real-time fault detection and remediation using advanced hybrid machine learning models,” Sustainability, vol. 15, no. 10, p. 8348, 2023.
  30. F. M. Shiri, T. Perumal, N. Mustapha, and R. Mohamed, “A comprehensive overview and comparative analysis on deep learning models: Cnn, rnn, lstm, gru,” ArXiv Preprint ArXiv:2305.17473, 2023.
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

Files

History

  • Receive Date: 29 September 2024
  • Revise Date: 25 December 2024
  • Accept Date: 30 December 2024
  • First Publish Date: 30 December 2024

Share

How to cite

Statistics