Modern electricity infrastructure is undergoing a transformation thanks to the combination of artificial intelligence (AI) and the Internet of Things (IoT), especially in the development of self- healing grid systems. In the context of smart grid automation and resilience, this article provides a thorough review of AI-driven IoT infrastructures. By leveraging distributed sensor networks, real-time data acquisition, and advanced KSK Approach, and machine learning algorithms—includes ANN, DT and K-NN—the proposed framework enables proactive fault detection, isolation, and service restoration (FDIR) with minimal human intervention. IoT devices facilitate seamless communication across distributed energy resources (DERs), smart meters, and phasor measurement units (PMUs), generating high-resolution operational telemetry. AI models process this big data to predict potential failures, optimize reconfiguration pathways, and dynamically balance load distribution under anomalous conditions. The system demonstrates enhanced situational awareness, reduced mean time to repair (MTTR), and improved grid stability through autonomous decision-making loops. Simulation results using IEEE 33-bus and 119-bus test systems validate the efficacy of the AI-IoT synergy in improving fault localization accuracy (>98%) and reducing outage durations by up to 75% compared to conventional SCADA-based approaches. This work underscores the transformative potential of intelligent, data-centric paradigms in evolving the electric grid into a self-sustaining, adaptive cyber- physical system.

Keywords - AI-driven IoT, Self-Healing Grid, Smart Grid Resilience, Edge Intelligence, Reinforcement Learning

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Aboukhris AB, Kolamroudi MK. Intelligent Control Strategy Optimization of Grid- Connected Hybrid PV-Battery-Supercapacitor Systems Using Machine Learning. InSmart Diagnostics and Predictive Maintenance 2026 Jan 2 (pp. 1-70). Singapore: Springer Nature Singapore.
2. Badakhshan S, Jacob RA, Li B, Wang P, Zhang J. Self-healing power systems using reinforcement learning over graphs for controlled grid islanding. Sustainable Energy, Grids and Networks. 2025 Aug 20:101937.
3. Wang Y, Zhao J, Tang D, Zhao W, Huang S. Intelligent fault prediction and diagnosis for wind-powered heating systems using graph neural networks. Scientific Reports. 2025 Nov 7;15(1):39068.
4. Liyakat KK. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. In2023 International Conference on Emerging Smart Computing and Informatics (ESCI) 2023 Mar 1 (pp. 1-5). IEEE.
5. Rana S. AI-driven fault detection and predictive maintenance in electrical power systems: A systematic review of data-driven approaches, digital twins, and self-healing grids. American Journal of Advanced Technology and Engineering Solutions. 2025 Feb 3;1(01):258-89.
6. Akhtar MH, Ghafoor U, Imran O, Ayub N, Abdullah MM, Khan H. An Efficient AI and Deep learning Assisted Self-Healing Network Approach: Analysis on Fault Detection Response and Recovery to Mitigate Threats in IoT-Security Ecosystem. The Asian Bulletin of Big Data Management. 2026 Jan 31;6(1):40-66.
7. Jagan S, Rachapalli A, Rajyalakshmi C, Yasmine Begum A, Prasad VV. AI-Driven Development of Smart Grid Infrastructure with Self-Healing Capabilities and Edge and Fog Computing. InProceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024) 2024 Nov 15.
8. Jain M, Prasad P. Self-Healing Networks with AI-Based Fault Prediction in IoT Ecosystems. International Journal of Scientific Research and Engineering Trends. 2025;11(10.61137).
9. Kazi KS. AI-Driven-IoT (AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. InAI-Driven Innovation in Healthcare Data Analytics 2025 (pp. 277-306). IGI Global Scientific Publishing.
10. Shankari M, Singh S, Balusamy B, Sridhar S. A Hybrid AI Framework for Real-Time Fault Detection and Self-Healing in Renewable-Rich Smart Grids. In2025 IEEE 4th International Conference for Advancement in Technology (ICONAT) 2025 Sep 19 (pp. 1-6). IEEE.
11. Feng J, Yu T, Zhang K, Cheng L. Integration of multi-agent systems and artificial intelligence in self-healing subway power supply systems: Advancements in fault diagnosis, isolation, and recovery. Processes. 2025 Apr 10;13(4):1144.
12. Vadisetty R, Polamarasetti A, Butani JB, Prajapati S, Raghunath V, Jyothi VK, Kudithipudi K. AI-Powered Self-Healing and Fault-Tolerant Cloud Infrastructures for Improved Resilience and Reliability. Available at SSRN 5286332. 2024 Sep 1.
13. Tamrakar G, Dusi P. AI-Driven Intelligent Control Frameworks for Enhancing Reliability, Resilience, and Efficiency in Smart Grids. National Journal of Intelligent Power Systems and Technology. 2025 Oct 20:10-6.
14. Kazi KS. AI-Powered-IoT (AIIoT)-Based Decision-Making System for BP-Patient Healthcare Monitoring: BP-Patient Health Monitoring Using KSK Approach. InTransforming Pharmaceutical Research With Artificial Intelligence 2025 (pp. 189- 218). IGI Global Scientific Publishing.
15. Zahid M, Munir HM, Adeel M, Alromithy FS, Altimania MR, Zaitsev I. AI‐Driven Optimization Techniques for Power Quality Improvement in Microgrids: Trends, Techniques, and Future Directions. Energy Science & Engineering. 2026 Jan;14(1):583- 610.
16. Abdaldaem I, Alskekh B, Mady Z. Advancing Power Quality in distribution grids through AI: Opportunities, challenges, optimization, and policy pathways. Int. J. Electr. Eng. and Sustain.. 2025 Sep 4:48-61.
17. Al-Jame F, Ali W. AI-Driven Smart Grid Architectures: IoT, Blockchain, and Renewable Energy Integration for Sustainable Power Systems. National Journal of Intelligent Power Systems and Technology. 2025 Oct 20:37-43.
18. Nuruzzaman M, Limon GQ, Chowdhury AR, Khan MM. Predictive maintenance in power transformers: A systematic review of AI and IoT applications. ASRC Procedia: Global Perspectives in Science and Scholarship. 2025 Apr 29;1(01):34-47.

Modern electricity infrastructure is undergoing a transformation thanks to the combination of artificial intelligence (AI) and the Internet of Things (IoT), especially in the development of self- healing grid systems. In the context of smart grid automation and resilience, this article provides a thorough review of AI-driven IoT infrastructures. By leveraging distributed sensor networks, real-time data acquisition, and advanced KSK Approach, and machine learning algorithms—includes ANN, DT and K-NN—the proposed framework enables proactive fault detection, isolation, and service restoration (FDIR) with minimal human intervention. IoT devices facilitate seamless communication across distributed energy resources (DERs), smart meters, and phasor measurement units (PMUs), generating high-resolution operational telemetry. AI models process this big data to predict potential failures, optimize reconfiguration pathways, and dynamically balance load distribution under anomalous conditions. The system demonstrates enhanced situational awareness, reduced mean time to repair (MTTR), and improved grid stability through autonomous decision-making loops. Simulation results using IEEE 33-bus and 119-bus test systems validate the efficacy of the AI-IoT synergy in improving fault localization accuracy (>98%) and reducing outage durations by up to 75% compared to conventional SCADA-based approaches. This work underscores the transformative potential of intelligent, data-centric paradigms in evolving the electric grid into a self-sustaining, adaptive cyber- physical system.

Keywords - AI-driven IoT, Self-Healing Grid, Smart Grid Resilience, Edge Intelligence, Reinforcement Learning

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Aboukhris AB, Kolamroudi MK. Intelligent Control Strategy Optimization of Grid- Connected Hybrid PV-Battery-Supercapacitor Systems Using Machine Learning. InSmart Diagnostics and Predictive Maintenance 2026 Jan 2 (pp. 1-70). Singapore: Springer Nature Singapore.
2. Badakhshan S, Jacob RA, Li B, Wang P, Zhang J. Self-healing power systems using reinforcement learning over graphs for controlled grid islanding. Sustainable Energy, Grids and Networks. 2025 Aug 20:101937.
3. Wang Y, Zhao J, Tang D, Zhao W, Huang S. Intelligent fault prediction and diagnosis for wind-powered heating systems using graph neural networks. Scientific Reports. 2025 Nov 7;15(1):39068.
4. Liyakat KK. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. In2023 International Conference on Emerging Smart Computing and Informatics (ESCI) 2023 Mar 1 (pp. 1-5). IEEE.
5. Rana S. AI-driven fault detection and predictive maintenance in electrical power systems: A systematic review of data-driven approaches, digital twins, and self-healing grids. American Journal of Advanced Technology and Engineering Solutions. 2025 Feb 3;1(01):258-89.
6. Akhtar MH, Ghafoor U, Imran O, Ayub N, Abdullah MM, Khan H. An Efficient AI and Deep learning Assisted Self-Healing Network Approach: Analysis on Fault Detection Response and Recovery to Mitigate Threats in IoT-Security Ecosystem. The Asian Bulletin of Big Data Management. 2026 Jan 31;6(1):40-66.
7. Jagan S, Rachapalli A, Rajyalakshmi C, Yasmine Begum A, Prasad VV. AI-Driven Development of Smart Grid Infrastructure with Self-Healing Capabilities and Edge and Fog Computing. InProceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024) 2024 Nov 15.
8. Jain M, Prasad P. Self-Healing Networks with AI-Based Fault Prediction in IoT Ecosystems. International Journal of Scientific Research and Engineering Trends. 2025;11(10.61137).
9. Kazi KS. AI-Driven-IoT (AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring. InAI-Driven Innovation in Healthcare Data Analytics 2025 (pp. 277-306). IGI Global Scientific Publishing.
10. Shankari M, Singh S, Balusamy B, Sridhar S. A Hybrid AI Framework for Real-Time Fault Detection and Self-Healing in Renewable-Rich Smart Grids. In2025 IEEE 4th International Conference for Advancement in Technology (ICONAT) 2025 Sep 19 (pp. 1-6). IEEE.
11. Feng J, Yu T, Zhang K, Cheng L. Integration of multi-agent systems and artificial intelligence in self-healing subway power supply systems: Advancements in fault diagnosis, isolation, and recovery. Processes. 2025 Apr 10;13(4):1144.
12. Vadisetty R, Polamarasetti A, Butani JB, Prajapati S, Raghunath V, Jyothi VK, Kudithipudi K. AI-Powered Self-Healing and Fault-Tolerant Cloud Infrastructures for Improved Resilience and Reliability. Available at SSRN 5286332. 2024 Sep 1.
13. Tamrakar G, Dusi P. AI-Driven Intelligent Control Frameworks for Enhancing Reliability, Resilience, and Efficiency in Smart Grids. National Journal of Intelligent Power Systems and Technology. 2025 Oct 20:10-6.
14. Kazi KS. AI-Powered-IoT (AIIoT)-Based Decision-Making System for BP-Patient Healthcare Monitoring: BP-Patient Health Monitoring Using KSK Approach. InTransforming Pharmaceutical Research With Artificial Intelligence 2025 (pp. 189- 218). IGI Global Scientific Publishing.
15. Zahid M, Munir HM, Adeel M, Alromithy FS, Altimania MR, Zaitsev I. AI‐Driven Optimization Techniques for Power Quality Improvement in Microgrids: Trends, Techniques, and Future Directions. Energy Science & Engineering. 2026 Jan;14(1):583- 610.
16. Abdaldaem I, Alskekh B, Mady Z. Advancing Power Quality in distribution grids through AI: Opportunities, challenges, optimization, and policy pathways. Int. J. Electr. Eng. and Sustain.. 2025 Sep 4:48-61.
17. Al-Jame F, Ali W. AI-Driven Smart Grid Architectures: IoT, Blockchain, and Renewable Energy Integration for Sustainable Power Systems. National Journal of Intelligent Power Systems and Technology. 2025 Oct 20:37-43.
18. Nuruzzaman M, Limon GQ, Chowdhury AR, Khan MM. Predictive maintenance in power transformers: A systematic review of AI and IoT applications. ASRC Procedia: Global Perspectives in Science and Scholarship. 2025 Apr 29;1(01):34-47.