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<p>The rise of urban IoT-enabled smart grids is transforming modern power systems, yet dynamic fault patterns and cyber-physical vulnerabilities continue to pose significant challenges. Traditional fault detection systems, such as Support Vector Machines (SVM), often lack the adaptability and speed required to address these complexities. To overcome these limitations, this research presents the Adaptive Fault Detection and Prevention System (AFDPS), a novel solution integrating Graph Attention Networks (GATs) with Neuro-Blockchain Integration (NBI) to enable secure, real-time fault detection and management in IoT networks. AFDPS enhances grid functionality by combining dynamic fault analysis with decentralized trust mechanisms, improving resilience and operational continuity in urban smart grids. The system has been empirically validated using MATLAB/Simulink for fault modeling and Ethereum blockchain for real-time security implementation. Results show that AFDPS achieves 96.8% fault detection accuracy, reduces system recovery time to 1.5&#xa0;s, and improves IoT node trust scores by 35%. This integrated approach establishes a new standard in smart grid fault management, paving the way for more robust and secure energy infrastructures.</p>

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Adaptive Smart Grid Fault Detection and Prevention in Urban IoTNetworks Using AI and Neuro-Blockchain Integration

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Anushree A. Aserkar,
  • K. Chanthirasekaran,
  • P. Anitha,
  • Gaurav Goyal,
  • Vivek Kothuru,
  • G. Indira Kishore

摘要

The rise of urban IoT-enabled smart grids is transforming modern power systems, yet dynamic fault patterns and cyber-physical vulnerabilities continue to pose significant challenges. Traditional fault detection systems, such as Support Vector Machines (SVM), often lack the adaptability and speed required to address these complexities. To overcome these limitations, this research presents the Adaptive Fault Detection and Prevention System (AFDPS), a novel solution integrating Graph Attention Networks (GATs) with Neuro-Blockchain Integration (NBI) to enable secure, real-time fault detection and management in IoT networks. AFDPS enhances grid functionality by combining dynamic fault analysis with decentralized trust mechanisms, improving resilience and operational continuity in urban smart grids. The system has been empirically validated using MATLAB/Simulink for fault modeling and Ethereum blockchain for real-time security implementation. Results show that AFDPS achieves 96.8% fault detection accuracy, reduces system recovery time to 1.5 s, and improves IoT node trust scores by 35%. This integrated approach establishes a new standard in smart grid fault management, paving the way for more robust and secure energy infrastructures.