Prediction of Transformer Oil Insulation Strength Based on Physical Quantities Using Ensemble Machine Learning Model
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Keywords
transformer oil, breakdown voltage, ensemble machine learning, Long Short-Term Memory (LSTM), prediction
Abstract
Existing machine learning models for predicting transformer oil breakdown voltage (BDV) often depend on chemical properties, which require laboratory tests, thus making them time-consuming and costly. Furthermore, these models are typically developed using standalone methods, which may fail to achieve satisfactory prediction accuracy. This study introduces an interpretable ensemble prediction model for transformer oil BDV, leveraging physical properties instead of chemical properties. The ensemble model integrates four standalone models: multilinear regression (MLR), multilayer perceptron (MLP) neural network, support vector machine (SVM), and long short-term memory (LSTM) neural network, to enhance prediction accuracy. A Local Interpretable Model-Agnostic Explanation framework was used to provide insights into the model's predictions. The model's performance was assessed using key metrices, including the correlation coefficient (R), determination coefficient (R²), root mean squared error (RMSE), and mean absolute error (MAE). Results revealed that the ensemble model outperformed individual models, achieving R = 0.9796, R² = 0.9596 during training and R = 0.9781, R² = 0.9567 during testing, along with RMSE = 1.7678, MAE = 1.2972 during training, and RMSE = 1.7347, MAE = 1.2985 during testing. The proposed model reliably predicted transformer oil BDV using physical properties, eliminating complex experiments while offering transparency to enhance applicability in the power industry
References
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L. Cheng and T. Yu, "A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems," International Journal of Energy Research, vol. 43, no. 6, pp. 1928-1973, 2019.
G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, "Linear Regression," in An Introduction to Statistical Learning: with Applications in Python. Cham: Springer International Publishing, 2023, pp. 69-134.
Y. Joo, D. Kim, Y. Noh, J. Choi, and J. Lee, "Performance Comparison of LSTM and ESN Models in Time-Series Prediction of Solar Power Generation," Sustainability, vol. 17, no. 19, p. 8538, 2025, doi: https://doi.org/10.3390/su17198538.
Y. Joo, D. Kim, Y. Noh, J. Choi, and J. Lee, "Performance Comparison of LSTM and ESN Models in Time-Series Prediction of Solar Power Generation," Sustainability, vol. 17, no. 19, p. 8538, 2025, doi: https://doi.org/10.3390/su17198538.
N. Bashir, H. Ahmad, and M. S. Suddin, “Ageing studies on transmission line glass insulators using dielectric dissipation factor test,” 2010 9th International Power Energy Conference (IPEC), Singapore, 27 – 29 October 2010: IEEE, pp. 1062–1066.
H. L. Willis, Electrical Power Equipment Maintenance and Testing, 2nd ed. London: CRC Press, 2009.
X. Wang, C. Tang, B. Huang, J. Hao, and G. Chen, "Review of research progress on the electrical properties and modification of mineral insulating oils used in power transformers," Energies, vol. 11, no. 3, p. 487, 2018.
I. S. Chairul, S. A. Ghani, N. A. Bakar, N. H. Rahim, and N. H. Ab Aziz, "Effect of acids formation on the breakdown voltage properties of transformer insulating oil," in 1st International Symposium on Engineering and Technology (ISETech), Perlis, Malaysia, 23 December 2019, vol. 767, no. 1: IOP Publishing, pp. 1-6.
Y. Chen, X. Wang, J. Shi, J. Zhao, and J. Zhang, "Transformer oil breakdown voltage prediction using an artificial neural network model with optimized input variables," Applied Sciences, vol. 11, no. 2552, pp. 346-372, 2021.
J. Liu, Y. Zhang, and J. Ma, "Power transformer fault detection and diagnosis using gas-in-oil analysis and machine learning algorithms," Journal of Electrical Engineering and Technology, vol. 12, no. 12, pp. 2245-2253, 2017.
O. E. Gouda and A. Z. El Dein, "Prediction of aged transformer oil and paper insulation," Electric Power Components and Systems, vol. 47, no. 4-5, pp. 406-419, 2019.
H. Nazar, A. Rashid, M. Shahzad, F. Khan, and B. Khan, "Optimization of the factors affecting the breakdown voltage of the transformer oil using genetic algorithm," in 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 3-4 March 2018: IEEE, pp. 1-6.
S. V. Kulkani, Transformer Engineering: Design, Technology, and Diagnostics, 2nd ed. London: CRC Press, 2013.
J. Sun and H. H. Hu, "An improved artificial neural network model for predicting breakdown voltage of transformer oil," Measurement, vol. 148, pp. 298-305, 2020.
M. Senoussaoui, M. Brahami, and I. Fofana, "Transformer Oil Quality Assessment Using Random Forest with Feature Engineering. Energies, 14, ," Energies, vol. 14, p. 1809, 2021.
A. Christina, M. A. Salam, Q. Rahman, F. Wen, S. Ang, and W. Voon, "Causes of transformer failures and diagnostic methods–A review," Renewable and Sustainable Energy Reviews, vol. 82, pp. 1442-1456, 2018.
R. Murugan and R. Ramasamy, "Understanding the power transformer component failures for health index-based maintenance planning in electric utilities," Engineering Failure Analysis, vol. 96, pp. 274-288, 2019.
B. W. Tuinema, J. L. Rueda-Torres, A. I. Stefanov, F. M. Gonzalez-Longatt, and M. A. M. M. V. D. Meihden, Probabilistic Reliability Analysis of Power Systems: A Student’s Introduction. Switzerland: Springer, 2020.
Y. Su, J. Zhang, Y. He, Y. Shu, M. Liu, and Q. Zhou, "Evaluating BDV in Transformer Oil Combined MFU and GRNN," in 2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE), Beijing, China, 6-10 September 2020: IEEE, pp. 1-4.
S. S. Ghoneim, S. S. Dessouky, A. A. Elfaraskoury, and A. B. A. Sharaf, "Prediction of insulating transformer oils breakdown voltage considering barrier effect based on artificial neural networks," Electrical Engineering, vol. 100, no. 4, pp. 2231-2242, 2018.
S. A. Khan, M. Tariq, M. Farhan, and Z. A. Khan, "Statistical techniques for prediction of breakdown voltage of liquid dielectrics used in power transformers," in 2023 International Conference on Power, Instrumentation, Energy and Control (PIECON), Aligarh, India, 10-12 Feb 2023: IEEE, pp. 1-5.
S. Zandbaaf, M. R. K. Khorrami, and M. G. Afshar, "Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy," Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 273, p. 120999, 2022.
C. Y. Yang and D. Wu, "A hybrid model combining random forest algorithm and gradient boosting decision tree algorithm for transformer oil breakdown voltage prediction," Journal of Energy Engineering, vol. 147, pp. 102-112, 2021.
H. Chen and R. Liao, "Transformer oil breakdown voltage prediction based on improved adaptive particle swarm optimization algorithm and support vector machine," Journal of Intelligent and Fuzzy Systems, vol. 41, pp. 2023-2033, 2021.
I. Ahmad and U. Maqsood, "Prediction of transformer oil breakdown voltage using multiple linear regression analysis," in 3rd International Conference on Computer, Communication and Signal Processing, Sanya, China, 20-22 December 2020.
M. A. Wahab, M. Hamada, A. Zeitoun, and G. Ismail, "Novel modeling for the prediction of aged transformer oil characteristics," Electric Power Systems Research, vol. 51, no. 1, pp. 61-70, 1999.
Y. Xiong, "Prediction Model for Breakdown Voltage of Transformer Oil Based on Relative Transformation and Kernel Principal Component Analysis," Journal of System Simulation, vol. 30, no. 5, pp. 1657-1664, 2019.
M. A. A. Wahab, "Artificial neural network-based prediction technique for transformer oil breakdown voltage," Electric Power Systems Research, vol. 71, no. 1, pp. 73-84, 2004.
F. Guerbas et al., "Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis," Scientific Reports, vol. 14, no. 1, p. 9271, 2024.
A. O. Osofisan, 1st, Ed. Fundamental concept of Machine learning in Artificial Intelligence with Practical Examples. Abuja: TETFUND, 2022.
J. K. Solano Meza, D. Orjuela Yepes, J. Rodrigo-Ilarri, and M.-E. Rodrigo-Clavero, "Comparative analysis of the implementation of support vector machines and long short-term memory artificial neural networks in municipal solid waste management models in megacities," International Journal of Environmental Research and Public Health, vol. 20, no. 5, p. 4256, 2023.
L. Li and T. Zhang, "Research on Text Generation Based on LSTM " International Core Journal of Engineering, vol. 7, no. 525-535, 2021.
M. J. Jimenez-Navarro, M. Martinez-Ballesteros, F. Martínez-Álvarez, and G. Asencio-Cortés, "A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting," Journal of Big Data, vol. 10, no. 1, p. 80, 2023.
L. Cheng and T. Yu, "A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems," International Journal of Energy Research, vol. 43, no. 6, pp. 1928-1973, 2019.
G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, "Linear Regression," in An Introduction to Statistical Learning: with Applications in Python. Cham: Springer International Publishing, 2023, pp. 69-134.
Y. Joo, D. Kim, Y. Noh, J. Choi, and J. Lee, "Performance Comparison of LSTM and ESN Models in Time-Series Prediction of Solar Power Generation," Sustainability, vol. 17, no. 19, p. 8538, 2025, doi: https://doi.org/10.3390/su17198538.
Y. Joo, D. Kim, Y. Noh, J. Choi, and J. Lee, "Performance Comparison of LSTM and ESN Models in Time-Series Prediction of Solar Power Generation," Sustainability, vol. 17, no. 19, p. 8538, 2025, doi: https://doi.org/10.3390/su17198538.
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Published
Jul 31, 2026
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How to Cite
Indabawa, M. G., Umar, N. B., & Abdulkadir, R. A. (2026). Prediction of Transformer Oil Insulation Strength Based on Physical Quantities Using Ensemble Machine Learning Model. Jurnal Nasional Teknik Elektro, 15(2). https://doi.org/10.25077/jnte.v15n2.1605.2026
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Electrical Power and Energy
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Author Biography
Nouruddeen Bashir, Kano University of Science & Technology
High Voltage Engineering, Renewable Energy