Intelligent IoT-Cloud Integrated Wireless RF Framework for Transformer Fault Diagnosis and Predictive Maintenance

Intelligent IoT-Cloud Integrated Wireless RF Framework for Transformer Fault Diagnosis and Predictive Maintenance
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-5
Year of Publication : 2025
Author : Asmita Sharma, R.P. Agarwal, Rajesh Singh
DOI : 10.14445/22315381/IJETT-V73I5P110

How to Cite?
Asmita Sharma, R.P. Agarwal, Rajesh Singh, "Intelligent IoT-Cloud Integrated Wireless RF Framework for Transformer Fault Diagnosis and Predictive Maintenance," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.103-115, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P110

Abstract
Transformers play a significant role in the energy management aspect of distribution networks, particularly in the management of energy and voltage regulation. Defeated transformers result in an unsteady power supply, damaged equipment, and economic damage. Old-fashioned monitoring methods depend on excruciatingly slow manual checks that are only done on a reactive basis and are inefficient for advanced fault identification. The amplified need for dependable power highlights the necessity for real-time autonomous transformer efficiency monitoring. The incorporation of the IoT with cloud computing and wireless RF has changed the pace of industrial automation, allowing real-time data capture, monitoring, and maintenance from personnel geographically far away. IoT enables persistent tracking of crucial measurable figures like voltage, current, temperature, and humidity, while LoRa RF is the long-range, low-power radio ideal for electricity transformers situated in isolated regions. Data analytic tools hosted on the cloud easily visualize information, enabling faster maintenance and improved fault diagnosis accuracy. Traditional techniques like DGA, vibration monitoring, and thermal imaging used in fault diagnosis rest upon a feeble premise of slow detection of fault problems, expensive maintenance requirements, and poor flexibility. Our proposal seeks to address the aforementioned issues with an IoT-based Smart Transformer Monitoring System that utilizes machine learning for predictive maintenance, cloud analytics, and an almost limitless range achieved through LoRa RF. While traditional wireless technologies like ZigBee fail to work on transformers due to their range limitation, wired networks suffer from data loss and physical damage, offering no protection against these threats. Myriota will also implement a Transformer Health Monitoring Unit, which will be responsible for collecting real-time sensor data, and an Edge Gateway, which will process and transmit data to the ThingSpeak Cloud for visualization. An embedded Machine Learning model will analyze the data for early fault detection and maximize the reliability, cost, and scalability for optimal transformer monitoring. The goals of this framework are to reduce maintenance costs to improve grid stability and accomplish a higher level of power system management..

Keywords
Transformer Health Monitoring, IoT-Based Fault Diagnosis, LoRa RF Communication, Edge Gateway, ThingSpeak.

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