AI-Driven SCADA Monitoring and Protection System for Transformers Using IoT and Real-Time Analytics

Abstract

The reliability and efficiency of transformers are critical in modern power distribution systems. Traditional maintenance practices are often reactive and result in operational delays or equipment failure. This paper presents an AI-driven SCADA-based monitoring and protection system for transformers, integrated with Internet of Things (IoT) and real-time analytics to enable predictive maintenance and immediate fault response. The proposed system employs multiple sensors to monitor vital parameters such as temperature, voltage, current, and oil level. Sensor data is processed by an Arduino microcontroller and transmitted via an ESP8266 module to a SCADA interface, where it is visualized and analyzed. Machine learning algorithms identify fault patterns, enabling early detection and proactive intervention. The system features automated protection mechanisms, including cooling activation and relay-controlled disconnection during abnormal conditions. The IoT-enabled SCADA interface offers remote monitoring, live status updates, and historical trend analysis, enhancing safety, operational efficiency, and decision-making. This solution is scalable and suitable for smart grids and industrial applications.

Introduction

This paper proposes an AI-driven, IoT-enabled SCADA-based monitoring and protection system for transformers to enhance reliability, efficiency, and predictive maintenance in modern power distribution networks.

Key Features:

• Problem with Traditional Methods: Conventional transformer maintenance is reactive, often leading to delays or failures.

• Proposed Solution: A real-time monitoring system using:

  • Sensors to measure temperature, voltage, current, and oil level.

  • Arduino microcontroller to process sensor data.

  • ESP8266 Wi-Fi module to transmit data to a SCADA interface.

• AI Integration:

  • Machine learning algorithms analyze sensor data to detect fault patterns.

  • Enables predictive maintenance and early intervention.

• Automated Protection:

  • Cooling systems and relays are activated automatically under abnormal conditions to prevent damage.

• IoT and SCADA Integration:

  • Enables remote monitoring, real-time updates, and historical data analysis.

  • Supports better decision-making and operational safety.

Benefits:

• Increases transformer lifespan.

• Enhances grid reliability and system efficiency.

• Scalable for smart grid and industrial applications.

Conclusion

This paper presents a comprehensive AI-driven SCADA monitoring and protection system for power transformers, integrating IoT and real-time analytics to enhance reliability, safety, and efficiency in modern power distribution networks. By leveraging a network of sensors, microcontroller-based data processing, and wireless communication via the ESP8266 module, the system provides continuous, real-time monitoring of critical transformer parameters. The integration of artificial intelligence and machine learning allows the system to go beyond traditional monitoring, enabling predictive maintenance and early fault detection based on historical data patterns. The implementation of automated protection mechanisms, such as relay-based load isolation and cooling activation, further strengthens the system’s ability to respond to abnormal conditions without requiring human intervention. The user-friendly SCADA interface offers live visualization, remote access, and historical trend analysis, making the system highly practical for smart grid and industrial applications. Overall, the proposed solution significantly reduces downtime, improves maintenance strategies, and ensures an uninterrupted power supply, thereby contributing to the modernization and resilience of energy infrastructure.

References

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Copyright

Copyright © 2025 B. Murugan, B. Shoba, A. Venugopal, M. Agilan, V. Dhinesh, R. Balamurugan. 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.