Machine Learning and M2M Communication in Smart Grids: A Review, Taxonomy, and Future Directions for Fault Management

References

[1] Srivastava, I., Bhat, S., Vardhan, B., & Bokde, N. (2022). Fault detection, isolation and service restoration in modern power distribution systems: A review. Energies, 15(19), 7264. https://doi.org/10.3390/en15197264

[2] Vaish, R., Dwivedi, U., Tewari, S., & Tripathi, S. M. (2021). Machine learning applications in power system fault diagnosis: Research advancements and perspectives. Engineering Applications of Artificial Intelligence, 106, 104504.

[3] Hu, J., He, H., & Blaabjerg, F. (2023). Fault location and classification for distribution systems based on deep graph learning methods. Journal of Modern Power Systems and Clean Energy. https://doi.org/10.35833/MPCE.2022.000204

[4] Zhang, Y., Qiu, F., Hong, T., Wang, Z., & Li, F. (2022). Hybrid imitation learning for real-time service restoration in resilient distribution systems. IEEE Transactions on Industrial Informatics, 18(4), 2089-2099. https://doi.org/10.1109/TII.2021.3078110

[5] Wang, Y., Qiu, D., & Strbac, G. (2022). Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems. Applied Energy, 310, 118575. https://doi.org/10.1016/j.apenergy.2022.118575

[6] Li, Q., Luo, H., Cheng, H., Deng, Y., Sun, W., Li, W., & Liu, Z. (2023). Incipient fault detection in power distribution system: A time-frequency embedded deep-learning-based approach. IEEE Transactions on Instrumentation and Measurement, 72, 1-12.

[7] Nguyen, B. L. H., Vu, T., Nguyen, T.-T., Panwar, M., & Hovsapian, R. (2022). Spatial-temporal recurrent graph neural networks for fault diagnostics in power distribution systems. IEEE Access, 10, 1-1.

[8] Chen, Z., Cai, S., & Meliopoulos, A. P. S. (2024). A real-time deep learning-based fault diagnosis framework in power distribution systems with PVs. IEEE PES Innovative Smart Grid Technologies (ISGT), Washington DC, Feb 19-22, 2024.

[9] Maurya, P., Vidhate, D., Nayak, R., Madhavi, P., Gawande, P., & Roshan, R. (2024). Self-healing grids: AI techniques for automatic restoration after outages. Power System Technology.

[10] Almasoudi, F. M. (2023). Enhancing power grid resilience through real-time fault detection and remediation using advanced hybrid machine learning models. Sustainability, 15(11), 8723. https://doi.org/10.3390/su15118723

[11] Arsoniadis, C. G., & Nikolaidis, V. C. (2024). A machine learning based fault location method for power distribution systems using wavelet scattering networks. Sustainable Energy, Grids and Networks, 40, 101551. https://doi.org/10.1016/j.segan.2024.101551

[12] Mamuya, Y. D., Lee, Y. D., Shen, J. W., Shafiullah, M., & Kuo, C. C. (2020). Application of machine learning for fault classification and location in a radial distribution grid. Applied Sciences, 10(14), 4965.

[13] Liang, W., Zhao, Y., Zhang, Z., You, Y., & Li, Y. (2023). Hybrid artificial intelligence for power grid line fault diagnosis and restoration auxiliary decision-making. 2023 5th Asia-Pacific Conference on Electrical Power and Energy Engineering (ACPEE).

[14] Sampaio, R., Melo, L., Leão, R., Barroso, G., & Bezerra, J. (2017). Automatic restoration system for power distribution networks based on multi-agent systems. IET Generation, Transmission & Distribution, 11(4), 475-484.

[15] Shafiullah, M., Abido, M., & Abdel-Fattah, T. (2018). Distribution grids fault location employing ST-based optimised machine learning approach. Energies, 11(11), 3033.

[16] Shafiullah, M., Alshumayri, K., & Alam, M. (2022). Machine learning tools for active distribution grid fault diagnosis. Advances in Engineering Software, 173, 103180.

[17] Liu, Y., Fan, R., & Terzija, V. (2016). Power system restoration: A literature review from 2006 to 2016. Journal of Modern Power Systems and Clean Energy, 4(3), 332-341.

[18] Zaben, M. M., Worku, M. Y., Hassan, M. A., & Abido, M. (2020). Machine learning methods for fault diagnosis in AC microgrids: A systematic review. IEEE Access, 8, 202141-202158.

[19] Rafique, F., Fu, L., & Mai, R. (2021). End-to-end machine learning for fault detection and classification in power transmission lines. Electric Power Systems Research, 199, 107430.

[20] Singh, A. R., Kumar, R. S., Bajaj, M., Khadse, C. B., & Zaitsev, I. (2024). Machine learning-based energy management and power forecasting in grid-connected microgrids. Scientific Reports, 14, 19207.

[21] Khoudry, E., Belfqih, A., Ouaderhman, T., Boukherouaa, J., & Elmariami, F. (2020). A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6486-6495.

[22] Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., & Xu, X. (2023). Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors, 23(3), 1502.

[23] Omitaomu, O., & Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548-568.

[24] Shakiba, F. M., Azizi, S. M., Zhou, M., & Abusorrah, A. (2022). Application of machine learning methods in fault detection and classification of power transmission lines: A survey. Artificial Intelligence Review, 55(3), 2315-2375.

[25] Mbamaluikem, P. O., Awelewa, A., & Samuel, I. (2018). An artificial neural network-based intelligent fault classification system for the 33-kV Nigeria transmission line. 2018 IEEE PES/IAS PowerAfrica, 1-5.

[26] Olalekan, H. W., Augustine, O. I., & Mathurine, G. (2024). Artificial neural network-based fault detection on Nigerian 330 kV power transmission line. International Journal of Research and Review, 11(10), 518-535. https://doi.org/10.52403/ijrr.20241047

[27] Peter, V. M., Okpura, N., & Udofia, K. (2025). Intelligent fault diagnosis in 330 kV power networks using SVM and ANN techniques: Case of the Onitsha--New Haven route. Journal of Engineering Research and Reports, 27(1), 1-14.

[28] Ekanem, N. U., Umoren, M., & Udofia, K. (2025). Reinforcement learning-assisted voltage stability analysis of the Nigerian power grid using DVR and BESS. Journal of Engineering Research and Reports.

[29] Oruma, A. M., Mahmud, I., Adamu, U. A., Wakawa, S. Y., Idris, G., & Mustapha, M. (2024). Fault detection method based on artificial neural network for 330 kV Nigerian transmission line. International Journal of Innovative Science and Research Technology (IJISRT).

[30] Ifeanyi, C. M., Ogbu, G., & Chukwu, L. (2025). Power system restoration using artificial neural network (ANN). American Journal of Multidisciplinary Research and Innovation, 4(3), 233-243.

[31] Ali, H., Solomon, C., Edward, M. B., Suresh Babu, K., Smerat, A., Alam, T., Sabirov, S., & Sengan, S. (2026). Deep reinforcement learning for real-time energy dispatch in smart grids with high renewable penetration. Clean Energy Science and Technology, 4(1). https://doi.org/10.18686/cest633

[32] Yin, T., Wulff, S., Pierre, J. W., & Amidan, B. (2024). Event detection and classification using machine learning applied to PMU data for the Western US Power System. IEEE Smart Grid Synchronized Measurement Applications (SGSMA). https://doi.org/10.1109/SGSMA58694.2024.10571471

[33] Lal, M. D., & Varadarajan, R. (2023). A review of machine learning approaches in synchrophasor technology. IEEE Access, 11, 33520-33541.

[34] A survey on IoT-based smart electrical systems. Energies, 19(4), 965. https://doi.org/10.3390/en19040965

[35] Vahidi, S., Ghafouri, M., Au, M., et al. (2023). Security of wide-area monitoring, protection, and control (WAMPAC) systems of the smart grid: A survey on challenges and opportunities. IEEE Communications Surveys and Tutorials, 25(2), 1294-1335.

[36] A review of multi-microgrids operation and control from a cyber-physical systems perspective. Computers, 14(10), 409. https://doi.org/10.3390/computers14100409

[37] Implementation of edge AI for early fault detection in IoT networks. Discover Applied Sciences. https://doi.org/10.1007/s43926-025-00196-4

[38] Chen, S.-J., Chiu, W.-Y., & Liu, W.-J. (2021). User preference-based demand response for smart home energy management using multiobjective reinforcement learning. IEEE Access, 9, 161627-161637.

[39] Amer, A. A., Shaban, K., & Massoud, A. M. (2023). DRL-HEMS: Deep reinforcement learning agent for demand response in home energy management systems. IEEE Transactions on Smart Grid, 14, 239-250.

[40] Alfaverh, F., & Denai, M. (2020). Demand response strategy based on reinforcement learning and fuzzy reasoning for home energy management. IEEE Access, 8, 39310-39321.

[41] Liu, W., Wang, Y., Jiang, F., Cheng, Y., Rong, J., Wang, C., & Peng, J. (2021). A real-time demand response strategy of home energy management by using distributed deep reinforcement learning. IEEE HPCC/DSS/SmartCity/DependSys, 988-995.

[42] Deep reinforcement learning for optimal microgrid energy management with renewable energy and electric vehicle integration. Applied Soft Computing, 2025, 113180. https://doi.org/10.1016/j.asoc.2025.113180

[43] Optimal energy management in smart energy systems: A deep reinforcement learning approach. Energy Reports, 2024. https://doi.org/10.1016/j.egyr.2024.11.024

[44] Digital twin-driven identification of fault situation in distribution networks connected to distributed wind power. International Journal of Electrical Power & Energy Systems, 2024. https://doi.org/10.1016/j.ijepes.2023.109726

[45] A review on digital twins for power generation and distribution. Journal of Cyber Security and Mobility, 2023. https://doi.org/10.1007/s10207-023-00784-x

[46] Introduction to digital twins for the smart grid. arXiv, 2025. https://arxiv.org/abs/2602.14256

[47] Tzanis, N., Andriopoulos, N., Magklaras, A., Mylonas, E., Birbas, M., & Birbas, A. (2020). A hybrid cyber-physical digital twin approach for smart grid fault prediction. IEEE Conference on Industrial Cyberphysical Systems (ICPS), 393-397. https://doi.org/10.1109/ICPS48405.2020.9274723

[48] You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital twins-based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305, 117899. https://doi.org/10.1016/j.apenergy.2021.117899

[49] Zhang, Y., Luo, J., Zhu, W., Wu, Y., & Zhang, X. (2022). Application of digital twins in smart grids. IEEE ICPICS, 9873758. https://doi.org/10.1109/ICPICS55264.2022.9873758

[50] Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). Development of a PSS for smart grid energy distribution optimization based on digital twin. Procedia CIRP, 107, 1138-1143. https://doi.org/10.1016/j.procir.2022.05.121

[51] Xing, J., Sun, S., Yu, P., Li, Y., Cheng, Y., Wang, Y., Li, S., & Zhu, J. (2022). Multi-energy simulation and optimal scheduling strategy based on digital twin. IEEE PSGEC, 9881079. https://doi.org/10.1109/PSGEC54663.2022.9881079

[52] DRL Home Energy Management Review, 2024; Applications of deep reinforcement learning for home energy management systems: A review. Energies, 17(24), 6420. https://doi.org/10.3390/en17246420

[53] DRL Renewable Energy Scheduling; Renewable energy scheduling for e-commerce systems. IEEE, 2021.

[54] DRL Smart Grid Scheduling, Smart grid scheduling with machine learning. IEEE, 2020.

@article{1717009,
      author = {Onwughalu Markanthony Kenechi, Eseosa Omorogiuwa, Ehikhamenle Matthew},
      title = {Machine Learning and M2M Communication in Smart Grids: A Review, Taxonomy, and Future Directions for Fault Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3684-3701},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717009.pdf},
      abstract = {The transformation of power distribution systems toward smart grid architectures has intensified the need for intelligent, communication-aware, and resilient fault management solutions, particularly in developing-region networks where reliability indices remain critically constrained. While machine learning techniques have demonstrated transformative capabilities in fault detection and classification, achieving superior accuracy through deep graph learning, spatial-temporal recurrent neural networks, and hybrid artificial intelligence approaches, these studies predominantly operate under idealised communication assumptions that ignore the latency, jitter, and packet loss inherent in real-world machine-to-machine deployments. Conversely, existing machine-to-machine communication protocols for smart grids, including LoRaWAN, NB-IoT, and ZigBee, have been studied in isolation from the diagnostic algorithms they are intended to support. This critical disconnect between algorithm accuracy and deployment reality creates a significant gap in the literature: no unified framework currently exists to evaluate machine learning performance under realistic machine-to-machine communication constraints or to optimise communication parameters for diagnostic reliability.This paper presents a comprehensive review of machine learning and machine-to-machine communication integration for low-voltage and medium-voltage distribution fault management, structured around a novel taxonomy of communication-aware architectures. We systematically analyse existing approaches across four categories: communication-agnostic machine learning, communication-assisted diagnostics, communication-resilient algorithms, and fully integrated machine-to-machine machine learning systems. Through comparative analysis of verified literature spanning deep reinforcement learning for service restoration, multi-agent coordination for automated switching, and federated learning for distributed intelligence, we identify critical research gaps, including the absence of electrical-communication co-simulation platforms, underdeveloped edge-based inference architectures, and insufficient validation under non-independent and identically distributed data conditions. We further propose a unified conceptual framework integrating electrical feeder dynamics, machine-to-machine communication impairments, and machine learning inference within a coordinated architecture, validated against Nigerian distribution network parameters as a representative developing-region case study. By consolidating existing knowledge and highlighting the imperative for communication-machine learning co-design, this work provides clear directions for advancing intelligent, resilient, and deployable fault management systems in next-generation distribution networks.},
      keywords = {Machine-To-Machine Communication; Machine Learning; Fault Detection; Distribution Networks; Smart Grids; Communication-Aware Architectures; Co-Simulation; Developing Regions},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1717009}
  }