Prof. Dr. Ir. Suwarno, Institut Teknologi Bandung, Indonesia

SUWARNO (Senior Member, IEEE) received the B.Sc. and M.Sc. degrees from the Department of Electrical Engineering, Institut Teknologi Bandung, Indonesia, in 1988 and 1991, respectively, and the Ph.D. degree from Nagoya University, Japan, in 1996.  Senior Member of IEEE. He is currently a Professor and the Emeritus Dean of the School of Electrical Engineering and Informatics, Institut Teknologi Bandung. He is also the Head of the Electrical Power Engineering Research Group. He has published more than 300 international journal articles and conference papers. His research interests include high voltage insulating materials and technology and diagnostics and asessment of HV equipment and renewable energy systems, and asset management. He was the General Chairperson of several international conferences, such as ICPADM 2006, ICEEI 2007, CMD 2012, ICHVEPS 2017, and ICHVEPS 2019-2025. He is the Editor-in-Chief of International Journal on Electrical Engineering and Informatics.

Artificial Intelligence Application for Condition Assessment of High Voltage Power Transformer using Health Index
Abstract: Power transformers are vital and costly components of the electrical power system. Ensuring the reliability and safety of these systems necessitates consistent monitoring and maintenance of transformers. The Health Index (HI) is a widely used method for assessing transformer conditions. This study models a method for calculating the transformer Health Index using artificial intelligence (machine learning) algorithms. The developed method considers transformer operating voltages and integrates dissolved gas analysis interpretations using multiple methods (Multi-methods). The findings indicate that higher operating voltages correlate with faster rates of transformer condition degradation. The non-conventional method with machine learning demonstrates higher prediction accuracy and reduces calculation subjectivity without requiring expert involvement. The Random Forest algorithm outperforms others in failure prediction based on DGA results, while Neural Networks excel in predicting HI categories with numerous input parameters. The application of the Synthetic Minority Oversampling Technique (SMOTE) improves model performance by balancing dataset classes, achieving a prediction accuracy of 99% for DGA and 97% for the Health Index.

Associate Professor Ir. Ts. Dr. Mohamad Nur Khairul Hafizi Rohani, Universiti Malaysia Perlis, Malaysia