IoT-Enabled Real-Time Monitoring and Loss-of-Life Estimation of Distribution Transformers

Abstract

A distribution transformer is required in power distribution networks to step down the voltage relevant and usable for consumers.  Its failure not only disrupts electricity supply but also incurs high replacement costs, with broader economic implications. Ensuring reliable operation, therefore, requires accurate and continuous monitoring of its performance. This paper presents IoT-Enabled Real-Time Monitoring and Loss-of-Life Estimation of Distribution Transformers developed and tested on a 10 kVA, 0.415 kV prototype distribution transformer, connected to three residential loads. A dedicated data acquisition system was developed, which monitors key parameters: load current, phase voltage, transformer oil level, ambient temperature, and oil temperature in real time over 14 days. An algorithm was implemented to analyze daily load profiles and hotspot temperature data, which were then used to estimate transformer loss of life. The results show that transformer ageing is highly sensitive to load variation. During weekdays, the cumulative equivalent ageing reached 2.22 hours per day, corresponding to a daily loss of life of 0.00296%. On weekends, higher residential loads increased cumulative ageing to 4.79 hours, with a corresponding life loss of 0.0063%. A simulated one-hour peak load of 1.43 pu resulted in 25.75 hours of ageing, translating to a life loss of 0.034%, demonstrating the severe impact of overloads. These findings emphasize that peak load periods dominate insulation ageing and can substantially reduce service life if unchecked.