Volume 02, Issue 6
Published on: June 2026
ADVANCED FAULT DETECTION AND SELF-HEALING STRATEGIES FOR SMART GRID RELIABILITY ENHANCEMENT
Arundhita Singh Sharad Kumar Sanjeev Kumar Kulshrestha
School of Engineering & Technology, Shri Venkateshwara University, Gajraula , U.P. India
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Abstract
Smart grids have emerged as a transformative solution for modern power distribution networks, offering enhanced efficiency, flexibility, and integration of renewable energy resources. However, the increasing complexity and interconnectedness of smart grid infrastructures make them highly vulnerable to faults, equipment failures, and operational disruptions that can compromise reliability and service continuity. To address these challenges, this paper presents an advanced fault detection and self-healing framework designed to improve smart grid reliability and resilience. The proposed approach integrates intelligent fault detection mechanisms with automated self-healing strategies to identify, isolate, and recover from faults in real time. Advanced data analytics and machine learning techniques are employed to monitor grid conditions, detect anomalies, and classify fault events with high accuracy. Upon fault identification, the self-healing module performs adaptive reconfiguration and restoration actions to minimize outage duration and maintain stable power delivery. The framework enables rapid decision-making, reduces manual intervention, and enhances the operational efficiency of the grid. Simulation-based evaluation demonstrates significant improvements in fault detection accuracy, response time, system availability, and network reliability compared to conventional fault management approaches. The findings highlight the potential of intelligent self-healing systems to support the development of robust, sustainable, and resilient smart grid infrastructures capable of meeting future energy demands.
Keywords—Smart Grid, Fault Detection, Self-Healing Systems, Machine Learning, Grid Reliability, Fault Isolation, Power Distribution Networks, Anomaly Detection, Automated Restoration.
Keywords—Smart Grid, Fault Detection, Self-Healing Systems, Machine Learning, Grid Reliability, Fault Isolation, Power Distribution Networks, Anomaly Detection, Automated Restoration.
How to Cite this Paper
Singh, A., Kumar, S. & Kulshrestha, S. K. (2026). Advanced Fault Detection and Self-Healing Strategies for Smart Grid Reliability Enhancement. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.176
Singh, Arundhita, et al.. "Advanced Fault Detection and Self-Healing Strategies for Smart Grid Reliability Enhancement." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.176.
Singh, Arundhita,Sharad Kumar, and Sanjeev Kulshrestha. "Advanced Fault Detection and Self-Healing Strategies for Smart Grid Reliability Enhancement." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.176.
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