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Transformer Internal and Inrush Current Fault Detection Using Machine Learning

R. Vidhya1,*, P. Vanaja Ranjan2, N. R. Shanker3

1 Department of Electronics and Communication Engineering, Loyola-ICAM College of Engineering and Technology, Chennai, 600034, India
2 Embedded System Technologies, Department of Electrical and Electronics Engineering, College of Engineering, Guindy, Chennai 600025, India
3 Department of Computer Science and Engineering, Aalim Muhammed Salegh College of Engineering, Chennai 600055, India

* Corresponding Author: R. Vidhya. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 153-168.


Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush or internal fault. The transformer’s internal fault leads to high breathing process in the transformer breather, never for inrush currents. During inrush currents, CT temperature is increased. Continuous monitoring of breather and CT of the transformer through thermal imaging and radiometric pixels detect the causes of CT saturation and differentiates maloperation. Hybrid wavelet threshold image analytics (HWT-IA) based radiometric pixels analysis of the transformer breather and CT after de-noising provides an accurate result of about 95% for identification of the false tripping of differential protection system of transformer.


Cite This Article

R. Vidhya, P. Vanaja Ranjan and N. R. Shanker, "Transformer internal and inrush current fault detection using machine learning," Intelligent Automation & Soft Computing, vol. 36, no.1, pp. 153–168, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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