Automatic Fault Detection and Isolation System in Distribution System

Abstract

Our project focuses on developing a cutting-edge, internet-based fault detection system for electrical distribution networks, aiming to address the critical need for rapid and accurate fault identification. Given the indispensable role of electricity in powering industries and daily life, particularly in urban areas, any disruption in the distribution system can have significant economic and operational repercussions. Our solution integrates advanced sensor technology with real-time data analysis and internet connectivity to swiftly detect and precisely locate faults within the distribution network. This innovative approach not only enhances the speed of fault detection but also minimizes downtime and expedites the restoration of power supply, thereby ensuring a more reliable and efficient electricity distribution system. By leveraging smart technology to streamline fault management, our project contributes to maintaining uninterrupted power flow and supporting economic stability and everyday convenience.

Introduction

Faults in power distribution systems, especially single-phase ground faults, cause abnormal current flow that can lead to equipment damage, safety hazards, and power outages. Detecting and isolating these faults quickly is crucial to maintain reliable power supply and reduce risks such as electric shock or fire.

Traditional fault detection methods like overcurrent relays are widely used but can be slow and less effective, particularly in small-current grounded systems. Advances in digital protection, smart grid technology, and IoT have improved fault detection accuracy and speed by enabling real-time monitoring and data transmission.

Modern systems integrate smart sensors, IoT devices, and machine learning algorithms to detect, locate, and isolate faults automatically. These systems use advanced techniques like impedance-based fault location, traveling wave analysis, and wavelet transforms. Self-healing networks can isolate faults and restore power to unaffected areas rapidly, minimizing outages.

The proposed system architecture includes:

• Smart sensors and IoT devices for real-time data collection.

• A centralized control unit for data processing using cloud and edge computing.

• Fault detection and isolation algorithms powered by machine learning to predict and precisely locate faults.

• Automated switchgear (reclosers and sectionalizers) for fast fault isolation and power restoration.

The methodology involves designing a scalable system with modular layers (sensor, communication, control, application), developing and training machine learning models on fault data, implementing automated isolation control, and deploying the system for real-world testing. This approach aims to improve fault detection speed, accuracy, and overall power distribution reliability.

Conclusion

Continuous Monitoring: With the help of this system we can continuous monitor the voltage and current of system. Earthing system protection and isolation: If the voltage and current sensors detect any discrepancies between the values of voltage and current, the system will isolate. Isolation of distribution system when fault is occur at supply side: If the voltages of line is less than 190 V then circuit breaker will in off position. similarly if the voltage of line is greater than the 260 V then the circuit breaker will be in off position this will be happen in by the use of sensors like current, voltage sensors. The Automatic Fault Detection and Isolation (FDI) System represents a significant advancement in the management of electrical distribution networks. By integrating IoT technology, machine learning, and real-time data analysis, the proposed system offers enhanced capabilities for fault detection, location, and isolation, addressing the critical need for reliability in modern power grids. his system marks a step forward in ensuring a more reliable, efficient, and smart distribution network, improving both the quality of service and the resilience of power supply systems in an increasingly connected and energy-dependent world.

References

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Copyright

Copyright © 2025 Prof. Pandurang M. Pujari , Prajakta Santosh Suryawanshi, Sanket Shantinath Khot, Prathmesh Rajendra Pharne, Aditya Arvind Patil. 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.