International Journal of Research in Advanced Electronics Engineering
2025, Vol. 6, Issue 2, Part A
Smart transformer design using IoT-enabled condition monitoring systems
Author(s): Arie Setiabudi Soesilo
Abstract: This study explores the integration of Internet of Things (IoT) technology into transformer design for real-time health monitoring and predictive maintenance. The IoT-enabled transformer system incorporates sensors to measure critical parameters such as temperature, dissolved gases, vibration, and partial discharge, enabling continuous condition monitoring. The study demonstrates the effectiveness of machine learning algorithms, such as Random Forest, in accurately detecting faults and predicting the remaining useful life (RUL) of transformers. With a fault detection accuracy of 93% and a strong correlation (R² = 0.92) between predicted and actual RUL, the IoT system significantly outperforms traditional maintenance methods that rely on offline testing. Additionally, the system achieved a 30% reduction in maintenance costs through early fault detection and optimized maintenance scheduling. This proactive approach enhances transformer reliability, reduces downtime, and provides cost savings. The findings suggest that integrating IoT technology into transformer systems not only improves diagnostic accuracy but also supports more efficient resource management and an extended transformer lifespan. Practical recommendations include designing transformers with embedded IoT systems from the outset, investing in staff training to interpret real-time data, and integrating predictive maintenance tools to optimize power grid operations.
Pages: 27-32 | Views: 11 | Downloads: 8
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How to cite this article:
Arie Setiabudi Soesilo. Smart transformer design using IoT-enabled condition monitoring systems. Int J Res Adv Electron Eng 2025;6(2):27-32.
