IOT Integrated Transformer Health Monitoring and Predictive Maintenance System

Abstract

Power distribution networks depend heavily on transformers, and their failure can seriously interrupt operations. In order to track transformer health and forecast maintenance requirements, this study presents a smart system that integrates IoT technologies. It makes defect detection and condition assessment possible in real time. The system uses a variety of sensors to track important metrics like voltage, current, power, energy, frequency, power factor, and temperature. Data is sent wirelessly through an ESP8266 to the ThingSpeak cloud, where it’s analyzed for any irregularities. A machine learning model, trained on historical sensor data, helps predict potential faults by using set thresholds, which aids in early detection and preventive maintenance. To make it even better, the system features a user-friendly dashboard built with Streamlit that offers real-time monitoring, fault classification, and instant alerts. It also explores different communication technologies like GSM, LoRa, Zigbee, and Bluetooth to ensure reliable data transmission. The Random Forest model achieves an impressive 99.2% classification accuracy, demonstrating the system\'s remarkable fault prediction accuracy. This method reduces maintenance costs, increases transformer dependability, and helps avoid power outages by combining IoT, cloud computing, and AI analytics. In the future, three-phase industrial transformers may benefit from improved scalability and faster fault detection with the integration of edge computing.

Introduction

Overview:
Transformers are critical in power grids, but 70–80% of failures result from internal issues like overheating and insulation breakdown. Traditional maintenance methods, such as periodic inspections, lack real-time fault detection. Modern Transformer Health Monitoring Systems (THMS) using IoT sensors and AI analytics enable early issue detection, improving safety, reliability, and transformer lifespan.

Prototype Development:

• Purpose: A scaled-down prototype was developed as a proof-of-concept to simulate real transformer environments. It integrates AI-based fault detection, sensor monitoring, and an interactive UI for proactive maintenance.

• Scalability: Though built for a single-phase transformer, the methodology is scalable to three-phase industrial systems.

• Phase-Level Monitoring: Detects specific faults (overload, underload, temperature shifts) using machine learning on real-time data.

Literature Review Highlights:

• Past studies explored various IoT platforms, sensors, and communication methods (e.g., GSM, Wi-Fi, ThingSpeak).

• Limitations included:

  • No AI-driven predictive maintenance

  • Focus on threshold-based alerts

  • Inadequate scalability and network stability in remote areas

• This research addresses these gaps by integrating ML prediction, IoT, and robust cloud platforms.

System Methodology:

1. Hardware Module:

• Uses sensors to measure voltage, current, power, frequency, power factor, and temperature.

• Data is sent via ESP8266 microcontroller to the cloud every 30 seconds.

• A load variation setup simulates real-world transformer stress.

• LCD displays real-time values on-site.

2. Cloud Platform (ThingSpeak):

• Stores and visualizes real-time sensor data.

• Enables remote monitoring, data analysis, and fault tracking.

• Supports historical trend analysis and comparison with real transformer values.

3. Machine Learning Model:

• Random Forest achieved 99.2% accuracy in fault detection.

• Trained with real sensor data for predicting:

  • Overheating & Overvoltage

  • Overvoltage

  • Undervoltage

  • Normal Operation

• Uses Pickle/Joblib for deployment.

• Hosted on GitHub for accessibility and future updates.

4. User Dashboard & Interface:

• Displays real-time transformer health data.

• Allows for intuitive monitoring and alert response.

Conclusion

This paper presents an IoT-integrated transformer health monitoring and predictive maintenance system, leveraging advanced communication technologies such as GSM, LoRa, Zigbee, and Bluetooth to enable real-time condition assessment. By incorporating multi-sensor fusion and AI-driven predictive analytics, the proposed system enhances fault detection, reduces downtime, and extends transformer lifespan. The reviewed studies highlight the effectiveness of IoT in improving reliability, efficiency, and automation in power distribution networks. Future work can focus on integrating edge computing and machine learning for further optimization, ensuring a more resilient and intelligent power grid.

References

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Copyright

Copyright © 2025 Dr. M. T. Hasan, Sanjana Surwade, Rushikesh Avinash Sarad, Ishika Vijay Taklikar, Reesha Ali, Vedant Lonare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.