Keywords
transformer health index
predictive maintenance
SCADA integration
machine learning
smart grid analytics
How to Cite
Abstract
Ensuring the continuous and reliable operation of power transformers is a fundamental requirement for modern electrical networks. The growing complexity of power systems demands innovative methods for real-time monitoring and predictive maintenance. This research presents an intelligent Internet of Things (IoT)-based system for continuous transformer condition assessment, integrating multi-sensor data acquisition, wireless transmission, and cloud analytics enhanced with machine learning algorithms.
The proposed model monitors vital parameters such as winding temperature, oil quality, and vibration levels. Data are analyzed using Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to evaluate the transformer’s health index dynamically. Statistical correlation reveals strong interdependence between temperature fluctuations, oil acidity, and insulation degradation. Experimental validation using real-time datasets from 110/35 kV substations demonstrated a predictive accuracy of 97.5%.
The integration of IoT-based analytics into SCADA environments enables predictive maintenance, reduces operational risks, and extends equipment lifespan. The results confirm the feasibility of transitioning from traditional maintenance strategies toward intelligent self-diagnosing power systems.
References
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