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All Journal International Journal of Engineering Continuity
Irnanda Priyadi
Department of Electrical Engineering, Universitas Bengkulu, Bengkulu, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Comparative Evaluation of Artificial Neural Networks and Monte Carlo Simulation for Transformer Insulating Oil Lifetime Prediction Irnanda Priyadi; Yuli Rodiah; Makmun Reza Razali; Shara Alya Gifani Muhyisunah
International Journal of Engineering Continuity Vol. 5 No. 1 (2026): IJEC
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v5i1.604

Abstract

Transformer insulating oil is an important factor for the reliability and service life of power transformers. It provides electrical insulation and heat dissipation. Accurate lifetime prediction is essential for asset management and condition-based maintenance. In this study, the comparison of Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS) techniques is presented to predict transformer insulating oil lifetime based on three physicochemical parameters such as acid content, moisture content and breakdown voltage. The model was developed and validated on an experimental dataset of 18 transformer insulating oil samples. The ANN model was based on a multilayer perceptron architecture with three hidden layers (80-80-40 neurons). The MCS model was run for 3000 simulation iterations to include the input uncertainty. The model performance was assessed using mean Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The ANN model produced better results with a MAPE of 8.20%, an RMSE of 4.15 months and an R2 of 0.98, surpassing the MCS model, which achieved a MAPE of 11.50%, an RMSE of 9.40 months and an R2 of 0.89. The results presented show that ANN is a more accurate and reliable methodology for the prediction of transformer insulating oil lifetime that allows efficient condition-based maintenance and transformer asset management.
Co-Authors Makmun Reza Razali Shara Alya Gifani Muhyisunah Yuli Rodiah