Transmission Line Fault Detection Using Iot

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Bol This proposed presents an IoT-based single-phase transmission line fault detection system that enables real- time monitoring and automated fault diagnosis.The proposed system consists of an ESP32 microcontroller, current and voltage sensors, and a cloud-based platform for remote data access. The sensors continuously measure electrical parameters, and the ESP32 processes this data to identify abnormalities. When a fault is detected, the system sends real-time alerts to a Firebase database or an IoT dashboard, allowing utility operators to take immediate corrective action. Additionally, a web or mobile application provides remote access to fault data, improving response efficiency. This IoT-driven approach enhances the reliability of power distribution networks by enabling early fault detection, remote monitoring, and reduced downtime. The system is scalable, cost-effective, and energy-efficient, making it a viable solution for modernizing fault detection in single-phase power systems.

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This proposed presents an IoT-based single-phase transmission line fault detection system that enables real- time monitoring and automated fault diagnosis.The proposed system consists of an ESP32 microcontroller, current and voltage sensors, and a cloud-based platform for remote data access. The sensors continuously measure electrical parameters, and the ESP32 processes this data to identify abnormalities. When a fault is detected, the system sends real-time alerts to a Firebase database or an IoT dashboard, allowing utility operators to take immediate corrective action. Additionally, a web or mobile application provides remote access to fault data, improving response efficiency. This IoT-driven approach enhances the reliability of power distribution networks by enabling early fault detection, remote monitoring, and reduced downtime. The system is scalable, cost-effective, and energy-efficient, making it a viable solution for modernizing fault detection in single-phase power systems.


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Merk LAP LAMBERT Academic Publishing
EAN
  • 9786209384400
Maat


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