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

• Author(s): Onwughalu Markanthony Kenechi; Eseosa Omorogiuwa; Ehikhamenle Matthew
• Paper ID: 1717009
• Page: 3684-3701
• Published Date: 01-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026

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

Citations

IRE Journals:
Onwughalu Markanthony Kenechi, Eseosa Omorogiuwa, Ehikhamenle Matthew "Machine Learning and M2M Communication in Smart Grids: A Review, Taxonomy, and Future Directions for Fault Management" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3684-3701 https://doi.org/10.64388/IREV9I10-1717009

IEEE:
Onwughalu Markanthony Kenechi, Eseosa Omorogiuwa, Ehikhamenle Matthew "Machine Learning and M2M Communication in Smart Grids: A Review, Taxonomy, and Future Directions for Fault Management" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717009