With the increasing demand for electrical power, preventing overload conditions is critical to ensuring safety and efficiency. This paper presents an IoT-based mains power cut system that monitors power consumption using an ACS712- 30A current sensor and automatically disconnects loads when a predefined threshold (135W) is exceeded. The system employs an ESP32 microcontroller, a 4-channel relay, and a WebSocket server for real-time control and monitoring. Users can manu- ally or automatically manage loads through a web-based user interface, integrated with ThingSpeak for data visualization. Experimental results demonstrate the effectiveness of the system in preventing overload situations.rms: IoT, Power Monitoring, Overload Protection, ACS712, ESP32, WebSocket, ThingSpeak.
Introduction
Effective power consumption monitoring is essential to prevent equipment failure, safety hazards, and energy inefficiencies. Traditional circuit breakers lack real-time monitoring and automation. The paper proposes an IoT-based system that provides real-time monitoring, automated load control, and overload prevention.
System Components:
ACS712 Current Sensor: Measures real-time current.
ESP32 Microcontroller: Processes data and controls relays.
WebSocket Server & ThingSpeak API: Enable real-time monitoring, logging, and visualization.
Electrical Loads: Monitors multiple loads such as LED bulbs, fans, and incandescent bulbs.
Methodology:
Current is measured continuously.
Power is calculated using P=V×I×PFP = V \times I \times PFP=V×I×PF.
Power is compared to a threshold of 135W.
Exceeding loads are disconnected automatically or alerts are sent in manual mode.
Real-time data is transmitted and displayed on a web interface; historical data is logged on ThingSpeak.
Results:
The system successfully identifies overloads and disconnects the highest-consuming load automatically.
Testing with different load combinations confirmed accurate power measurement and effective automation.
Example: Total consumption of 142W triggered automatic disconnection to maintain the threshold of 135W.
Conclusion
This paper presented an IoT-based mains power cut system that ensures overload protection through real-time monitoring and automation. The proposed system provides an efficient and user-friendly approach to managing power loads, reducing risks associated with excessive consumption. Future work will involve extending the system with machine learning for predictive analysis, improving energy efficiency through dy- namic load adjustments, and integrating adaptive thresholding mechanisms to enhance performance . In the future, the system can be enhanced with Implementing machine learning algorithms for predictive maintenance. Moreover, Integrating renewable energy sources for enhanced energy efficiency and lastly, developing a mobile application for better user interaction and remote access.
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