A Review on Technologies Used in Transformer Health Monitoring Systems

Abstract

Transformer health monitoring is a critical aspect of power system reliability, and numerous researchers have explored various technologies to enhance real-time monitoring and predictive maintenance. Reliable operation of distribution transformers is essential to achieve efficient power distribution. The conventional maintenance practices are reactive, which results in the faults being detected late, an increase in downtime, and higher operational expenditure. As communication technologies have improved, transformer health monitoring systems have been developed, which can offer real-time data collection and predictive maintenance, enhancing efficiency and reliability. This paper discusses different technologies employed for transformer condition monitoring, such as Bluetooth, GSM, Zigbee, LoRa, and other wireless communication technologies. Each technology has specific strengths regarding data transmission distance, power consumption, and network capacity. When these communication systems are combined with IoT sensors, the most important transformer parameters like temperature, voltage, current, and oil condition can be monitored continuously. The data is processed for fault prediction, allowing early warnings and preventive maintenance. The comparative study of these technologies identifies their applicability in various deployment scenarios based on coverage area, data rate, and cost-effectiveness. The paper also addresses how IoT and machine learning contribute to improving fault detection and maintenance optimization. Finally, the fusion of these technologies in transformer health monitoring systems can powerfully enhance operational efficiency, decrease failures, and prolong transformer lifespan.

Introduction

Summary:

Transformers consist of isolated primary and secondary copper windings and a magnetic core, operating on mutual induction to transfer power. They face various stresses such as thermal expansion, core vibrations, heating from eddy currents, and mechanical impacts due to faults or overloading. Faults are mainly internal (inside the transformer, causing insulation damage) or external (outside, cleared downstream), with internal faults causing 70-80% of failures. Transformers are vital for efficient power transmission, requiring real-time health monitoring to prevent costly outages and safety risks.

Transformer Health Monitoring Systems (THMS):
These systems use sensors and AI to continuously monitor key parameters like temperature, oil condition, voltage, current, and insulation. Unlike conventional scheduled maintenance, THMS enables real-time, predictive maintenance, detecting faults early to avoid failures, optimize operation, reduce downtime, and enhance safety.

Typical Faults Detected Early by THMS:

• Overheating and thermal stress

• Insulation degradation

• Oil contamination and moisture ingress

• Winding short circuits and electrical faults

• Vibration and mechanical deformation

Wireless Communication Technologies for THMS:
GSM, Bluetooth, Zigbee, LoRa, and MQTT are employed for data transmission. GSM is widespread for remote alerts, Zigbee suits short-range monitoring, LoRa enables long-range monitoring, and MQTT optimizes client-server communication with low bandwidth and power consumption. Combining AI with IoT enhances fault prediction accuracy.

Sensors Used in THMS:
Temperature sensors, Dissolved Gas Analysis (DGA), moisture sensors, partial discharge sensors, vibration sensors, voltage/current sensors, oil level and humidity sensors, pressure sensors, and load sensors—all critical for early fault detection and operational safety.

AI Integration Benefits:
AI enables improved fault diagnosis, real-time alerts, pattern recognition for predictive maintenance, reduction of false alarms, and optimized load management, transforming transformer maintenance from reactive to proactive.

Conclusion

Transformer Health Monitoring Systems (THMS) are essential for maintaining the reliability, efficiency, and lifespan of transformers in today\'s power networks. THMS utilize sophisticated sensor technologies to monitor critical parameters like temperature, dissolved gases, water content, and electrical loading in real-time. By picking up on early warning signs of incipient failure, THMS facilitates predictive maintenance, mitigating the risk of sudden failures, expensive repairs, and power outages. The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has further augmented the functionality of these systems to enable real-time data analysis and remote monitoring for enhanced operational effectiveness. Fault detection mechanisms in THMS are critical to detect problems such as overheating, insulation deterioration, and mechanical stress before they become major failures. Conventional maintenance practices, based on regular checks, tend to miss concealed faults that occur between servicing periods. With the implementation of smart monitoring technology, however, power utilities are able to move from reactive to predictive maintenance, aligning maintenance schedules for optimal efficiency and increasing the lifespan of transformers. Such a forward-looking approach not only improves safety but also reduces the cost of maintenance and minimizes the environmental footprint of transformer failures. In summary, the transformer health monitoring system review emphasizes the need for continuous monitoring, predictive analysis, and fault detection in contemporary power systems. The combination of advanced technologies guarantees transformers are run within secure parameters, enhancing energy efficiency and grid stability. With increasing demand for reliable electricity, investment in sophisticated health monitoring solutions will be crucial to guaranteeing the smooth operation of transformers and the viability of power infrastructure

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Copyright

Copyright © 2025 Sanjana Surwade, Reesha Ali, Ishika Taklikar, Rushikesh Sarad, Vedant Lonare, Dr Mohd. Tahseenul Hasan. 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.