STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Advanced Control and System Engineering Original Research

Voltage Vigilance: Ensuring Balance and curbing Theft in Power Distribution Networks

  1. Saran D S Department of Electrical & Electronics Engineering, S. A. Engineering College Chennai
  2. A. Prabha Department of Electrical & Electronics Engineering, S. A. Engineering College Chennai
  3. Monesh B Department of Electrical & Electronics Engineering, S. A. Engineering College Chennai
  4. Syed Wajeed Ahamed M Department of Electrical & Electronics Engineering, S. A. Engineering College Chennai

Abstract

This project proposes a system for detecting electrical overload and earth faults using an Arduino Uno controller. The system uses a current sensor to identify overloads and a voltage sensor to detect earth faults. An ESP8266 module allows users to switch between the two fault detection modes. When an overload is detected, the Arduino turns off AC bulbs using a relay and sends an alert message via a GSM module. Similarly, for an earth fault, the Arduino switches off the bulbs and sends an alert. The system includes an LCD screen to display status. This design enhances safety, provides real-time alerts, and is user-friendly. It is suitable for homes, industries, and commercial spaces, ensuring electrical safety, protecting equipment, and promoting efficient energy use. Efficient power distribution is critical for the sustainability and reliability of modern energy systems. However, challenges such as load imbalances and electricity theft significantly hinder operational efficiency, escalating costs, and undermine grid stability. This paper explores a comprehensive framework for ensuring balance and mitigating theft in power distribution networks using advanced monitoring, data analytics, and smart grid technologies. A two-pronged approach is proposed: first, employing real-time load balancing through dynamic demand-response mechanisms to optimize energy allocation; second, leveraging machine learning algorithms and Internet of Things (IoT)-based smart meters to detect anomalies indicative of theft. Case studies and simulation results demonstrate significant reductions in distribution losses and improved grid efficiency.

Keywords

References (15)

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