Development of a Compact Self-System with Real-Time Fault Detection, Automated Power Distribution, and ML-Driven Predictive Analysis

Amit Raikar, Soumya L. M., T. C. Manjunath

2025

Abstract

This project presents a compact self-healing grid system designed to automate fault detection, optimize power distribution, and provide real-time notifications and data for predictive analysis. The system leverages Arduino for grid management, an LCD for real-time display of grid status, switches to simulate faults, and a buzzer for audible fault alarms. Voltage sensors and current sensors continuously monitor the power system, while a NodeMCU (ESP8266) module facilitates the sending of real-time alerts via Telegram messaging. In the event of a fault, the system detects the anomaly, reroutes power to maintain supply, and sends alerts containing detailed voltage, current, and power data. The system is further enhanced by integrating machine learning (ML), which processes this data for predictive maintenance and fault detection, enabling grid operators to anticipate and prevent failures. By combining real-time monitoring, automatic rerouting, and ML-driven predictive capabilities, this self-healing grid system improves both the resilience and efficiency of power distribution networks, offering a scalable solution for grid automation and management.

Paper Citation

in Harvard Style

Raikar A., L. M. S. and Manjunath T. (2025). Development of a Compact Self-System with Real-Time Fault Detection, Automated Power Distribution, and ML-Driven Predictive Analysis. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 839-847. DOI: 10.5220/0013733800004664

in Bibtex Style

@conference{incoft25,
author={Amit Raikar and Soumya L. M. and T. C. Manjunath},
title={Development of a Compact Self-System with Real-Time Fault Detection, Automated Power Distribution, and ML-Driven Predictive Analysis},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={839-847},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013733800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Development of a Compact Self-System with Real-Time Fault Detection, Automated Power Distribution, and ML-Driven Predictive Analysis
SN - 978-989-758-763-4
AU - Raikar A.
AU - L. M. S.
AU - Manjunath T.
PY - 2025
SP - 839
EP - 847
DO - 10.5220/0013733800004664
PB - SciTePress