A Review on Arduino-Based Self-Healing Smart Grids with Real-Time Auto Fault Detection

Abstract

With the growing need for reliable, efficient, and uninterrupted power delivery, modern electrical grids are evolving into intelligent systems capable of self-monitoring and automated recovery. The survey work focuses on the different designs and implementation methods for Arduino-based self-healing smart grid systems that detect faults in real time, isolate affected sections, and restore regular operations with minimal human intervention. Using a network of voltage and current sensors, these systems continuously monitor the status of each distribution node. When an abnormality such as a voltage drops or current interruption is detected, the Arduino controller identifies the fault and activates relay switches to isolate the faulty line. This ensures that the rest of the grid continues to operate smoothly, improving overall system reliability. The proposed survey identifies the various low-cost and scalable approaches for implementing self-healing capabilities, making it suitable for small-scale grids and educational research environments.

Introduction

The growing demand for reliable and efficient electricity has highlighted the limitations of traditional power grids, including slow fault detection, inefficient energy distribution, and high downtime. Smart grids address these issues by integrating sensing, automation, and communication technologies, enabling real-time monitoring, fault detection, and autonomous restoration. Arduino microcontrollers provide a low-cost, flexible platform for implementing such systems, processing sensor data and controlling relays to isolate faulty sections automatically.

Various approaches to Arduino-based self-healing grids include:

1. Basic Fault Detection: Monitoring voltage and current with alerts for manual fault isolation.

2. Relay-Based Auto Isolation: Automatic disconnection of faulty sections to maintain uninterrupted supply.

3. IoT-Enabled Monitoring: Remote supervision and data logging via cloud platforms.

4. Multi-Sensor Fault Classification: Improved detection accuracy using multiple sensors and threshold-based algorithms.

5. Predictive Self-Healing Grids: Integration of real-time monitoring, automatic isolation, and predictive analytics for proactive fault prevention and optimized power management.

Conclusion

The analysis of various methodologies demonstrates that integrating microcontroller-based automation with intelligent sensing can significantly improve grid reliability and operational efficiency. By using voltage and current sensors, the system can monitor electrical parameters continuously, identify abnormal conditions, and respond promptly through relay-based isolation. The inclusion of IoT modules further enhances system functionality by enabling remote monitoring and cloud-based data analysis. Collectively, these advancements contribute to the creation of smarter and more resilient power distribution networks capable of self-recovery without extensive human intervention. The comparative evaluation of proposed methods shows that each stage of development adds a new dimension of intelligence and autonomy to the system. From simple fault detection in early models to advanced predictive control in IoT-enabled grids, the research trend clearly moves toward data-driven and adaptive power management. The results confirm that Arduino-based platforms provide a low-cost yet highly effective foundation for building scalable and modular self-healing grid prototypes.

References

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Copyright

Copyright © 2025 Syed Mustafa, Niranjan K, Kiran K N . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.