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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM.RACCES062
Volume:
10 |
4 |
April 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics and Communication Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Electronics and Communication Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Electronics and Communication Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Electronics and Communication Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Electronics and Communication Engineering
Bharathiyar Institute of Engineering for women (Autonomous) Salem, TamilNadu, India
Electric power distribution systems often face problems such as phase failures, voltage fluctuations, and electrical faults. These issues can lead to power interruptions and equipment damage. This paper proposes a Real-Time IoT Smart Grid Solution for Automatic Phase Selection and Fault Monitoring. The system continuously monitors the three-phase supply (R, Y, B) using voltage and current sensors. A microcontroller processes the sensor data and automatically selects the healthy phase using a relay module when a fault occurs. Additionally, an IoT module transmits real-time data to a cloud server, allowing remote monitoring of faults and power conditions. The proposed system improves reliability, reduces power interruption time, and enables efficient monitoring of the power distribution network.
KEYWORDS
This paper presents a Real-Time IoT Smart Grid Solution for Automatic Phase Selection and Fault Monitoring. The system improves reliability of power distribution by automatically detecting faulty phases and switching to a healthy phase. The integration of IoT technology allows remote monitoring of electrical parameters and system faults. This solution can significantly reduce power interruption time and improve efficiency in power management systems. • Mobile application for power monitoring • Integration with smart meters
1. Tilkar, K., Managre, J., Karketta, A., Rathod, D., & Makwana, G. (2024). Deploying an IoT-Enabled Smart Grid Automation System for Continuous Optimization and Fault Identification. Journal of Power Electronics and Power Systems, 14(1), 1–8. 2. Singh, A. (2026). Application of IoT in Smart Grid Fault Detection and Isolation. International Journal of Research in Modern Engineering & Emerging Technology (IJRMEET). 3. Prakash, K. M., Bhoomika, R., Chandana, H. M., Chinmay, B. G., & Manoj, R. K. (2026). IoT Based Three Phase Power Failure Monitoring with Alert System. International Journal for Research in Applied Science & Engineering Technology (IJRASET). 4. Shukla, A., Yadav, A., Singh, D., Yadav, S., & Gupta, R. K. (2025). IoT-Driven Smart Grid Resilience: Real-Time Fault Detection and Load Balancing. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT). 5. Bondre, S., Walkunde, Y., Salunkhe, R., & Bhoskar, N. G. (2024). IoT Innovations in Power System Monitoring: Reviewing Transmission Line Multiple Fault Detection Systems. Journal of Control & Instrumentation. 6. Goyal, G., Salsabil, I. T., Kumar, A., Ukey, M., & Velumani, P. S. (2025). AI-Driven Fault Detection and Diagnosis in Smart Grids for Enhanced Power System Reliability. Journal of Information Systems Engineering and Management. 7. Kang, Z., Zhang, Y., & Du, Y. (2024). Application of Multi-Sensor Information Fusion Technology in Fault Early Warning of Smart Grid Equipment. Energy Informatics Journal. 8. Kumar, S., Khan, M. M., Yadav, S. K., & Gupta, H. (2021). Automatic Fault Detection System with IoT. International Journal of Trend in Scientific Research and Development (IJTSRD). 9. Gungor, V. C., Sahin, D., Kocak, T., et al. (2013). Smart Grid Technologies: Communication Technologies and Standards. IEEE Transactions on Industrial Informatics. 10. De Santis, E., Livi, L., Sadeghian, A., & Rizzi, A. (2014). Modeling and Recognition of Smart Grid Faults Using Machine Learning Techniques. IEEE Smart Grid Research.
Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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