Power Theft Identification System using IOT

The detection and prevention of electricity theft are critical challenges in the management of power distribution networks. This study presents the design and implementation of an advanced Internet of Things (IoT)-based system for real-time detection of electricity theft. The system leverages an Arduino microcontroller interfaced with various sensors to monitor electrical parameters such as voltage, current, and power consumption. Data from these sensors are transmitted to a central server via wireless communication modules, including Wi-Fi and Zigbee, enabling continuous monitoring and data analysis. The core of the system is an intelligent algorithm that analyzes the incoming data to detect anomalies indicative of electricity theft. This algorithm incorporates machine learning techniques to improve detection accuracy and reduce false positives. When a potential theft is detected, the system automatically alerts the utility provider and triggers predefined actions, such as disconnecting the power supply to prevent further loss. Additionally, the system features a user-friendly interface that allows for remote monitoring and control through a web-based dashboard and mobile application. This interface provides real-time visualization of power usage data, historical analysis, and notifications of detected theft events. The integration of IoT and advanced data analytics ensures a robust, scalable, and cost-effective solution for addressing electricity theft.