Deep Learning-Based Classification of Transformer Inrush and Fault Currents Using a Hybrid Self-Organizing Map and CNN Model

Heungseok Lee, Sang-Hee Kang and Soon-Ryul Nam ()
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Heungseok Lee: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Sang-Hee Kang: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea
Soon-Ryul Nam: Department of Electrical Engineering, Myongji University, Yongin 17058, Republic of Korea

Energies, 2025, vol. 18, issue 20, 1-24

Abstract: Accurate classification between magnetizing inrush currents and internal faults is essential for reliable transformer protection and stable power system operation. Because their transient waveforms are so similar, conventional differential protection and harmonic restraint techniques often fail under dynamic conditions. This study presents a two-stage classification model that combines a self-organizing map (SOM) and a convolutional neural network (CNN) to enhance robustness and accuracy in distinguishing between inrush currents and internal faults in power transformers. In the first stage, an unsupervised SOM identifies topologically structured event clusters without the need for labeled data or predefined thresholds. Seven features are extracted from differential current signals to form fixed-length input vectors. These vectors are projected onto a two-dimensional SOM grid to capture inrush and fault distributions. In the second stage, the SOM’s activation maps are converted to grayscale images and classified by a CNN, thereby merging the interpretability of clustering with the performance of deep learning. Simulation data from a 154 kV MATLAB/Simulink transformer model includes inrush, internal fault, and overlapping events. Results show that after one cycle following fault inception, the proposed method improves accuracy (AC), precision (PR), recall (RC), and F1-score (F1s) by up to 3% compared with a conventional CNN model, demonstrating its suitability for real-time transformer protection.

Keywords: inrush current; internal fault; transformer differential protection; self-organizing map; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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