Integrated Digital Twins System for Oil Temperature Prediction of Power Transformer Based on Internet of Things

Aim:

         The Mainstay of the project is to develop an IoT-based real-time monitoring system for power transformers using an ESP32, enabling continuous tracking of temperature, oil level, voltage, and current while visualizing faults through a 3D model for proactive maintenance and improved reliability.

Introduction:

           Power failures and transformer malfunctions can lead to significant disruptions in daily life, affecting industries, businesses, and households. Unmonitored transformers are prone to overheating, oil depletion, and electrical faults, which can cause breakdowns, power outages, and even fire hazards. Timely detection of these issues is crucial to ensuring a stable and uninterrupted power supply.

         This project addresses the need for efficient and real-time transformer monitoring by leveraging IoT technology. By continuously tracking vital parameters such as temperature, oil level, voltage, and current, potential failures can be identified before they cause serious damage. The system sends real-time data to the cloud, allowing remote access and proactive maintenance.

          To enhance accessibility, a 3D visualization model dynamically represents transformer health, making it easier for operators to identify faults and take immediate action. By implementing such monitoring systems, we can reduce unexpected power failures, minimize repair costs, and ensure energy efficiency, ultimately benefiting both urban and rural communities.

        This solution is a step toward sustainable power management, reducing downtime and improving electricity distribution reliability. Widespread adoption of such smart monitoring systems can lead to a more resilient and energy-efficient future

Proposed System:

        To address the limitations of traditional transformer monitoring, this project introduces an IoT-based real-time monitoring system using an ESP32 as the main controller. Since this is a prototype, a 12V power supply board is used to simulate a transformer. The system integrates multiple sensors to track key parameters, including a water level sensor to detect oil levels, a temperature sensor to monitor overheating, and voltage and current sensors to measure power output. The collected data is continuously updated to the cloud, enabling remote access and proactive maintenance.

       A one-channel relay, powered by the simulated transformer, controls a 12V DC motor as a load. The cloud-based monitoring system ensures real-time data visualization, reducing the chances of undetected failures. A Node.js-based application retrieves data from the cloud and updates a 3D model of the transformer. This interactive model changes color based on sensor readings, providing an intuitive representation of the transformer’s health. If any fault occurs, such as low oil levels, overheating, or abnormal voltage/current fluctuations, the affected part of the 3D model changes color to alert operators.

      This system enhances efficiency, safety, and reliability by enabling predictive maintenance, reducing downtime, and ensuring an uninterrupted power supply, making it a cost-effective and scalable solution