Document Type : Original/Review Paper
Author
Department of Electrical Engineering, YI.C., Islamic Azad University, Tehran, Iran.
10.22044/jadm.2025.16214.2745
Abstract
Fault prediction in power transformers is pivotal for safeguarding operational reliability and reducing system disruptions. Leveraging dissolved gas analysis (DGA) data, AI‑driven techniques have recently been employed to enhance predictive performance. This paper introduces a novel machine-learning framework that integrates Hist Gradient Boosting (HGB) with a metaheuristic Particle Swarm Optimization (PSO) algorithm for hyperparameter tuning, thereby guaranteeing classifier robustness. The proposed method underwent a two‑stage evaluation: first, Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and HGB were benchmarked, revealing HGB as the most effective method; second, PSO was applied to optimize HGB’s hyperparameters, yielding further performance improvements. Experimental results demonstrate that the hybrid HGB‑PSO model achieves an accuracy of 97.85 %, precision of 98.90 %, recall of 97.33 %, and an F1‑score of 98.99 %. All simulations and comparative analyses against state‑of‑the‑art methods were implemented in Python, and confusion‑matrix analysis was employed to assess predictive performance comprehensively. These findings demonstrate that the hybrid HGB‑PSO method achieves superior accuracy and robustness in transformer fault prediction.
Keywords
Main Subjects
[1] X. Zheng, “Intelligent Fault Diagnosis of Power Transformer base on Fuzzy Logic and Rough Set Theory,” 7th World Congress on Intelligent Control and Automation, pp. 6858 - 6862, 2008.
[2] A. Nanfak, E. Samuel, I. Fofana, F. Meghnefi, M. G. Ngaleu, and C. H. Kom, “Traditional fault diagnosis methods for mineral oil‐immersed power transformer based on dissolved gas analysis: Past, present and future,” IET Nanodielectrics, vol. 7, no. 3, pp. 97–130, Apr. 2024.
[3] C. Aj, M. A. Salam, Q. M. Rahman, F. Wen, S. P. Ang, and W. Voon, “Causes of transformer failures and diagnostic methods – A review,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 1442–1456, Jul. 2017.
[4] Y. Zhang, Y. Tang, Y. Liu, and Z. Liang, "Fault diagnosis of transformer using artificial intelligence: A review," Frontiers in Energy Research, vol. 10, Art. no. 1006474, 2022.
[5] S. R. Al-Sakini, G. A. Bilal, A. T. Sadiq, and W. A. K. Al-Maliki, "Dissolved gas analysis for fault prediction in power transformers using machine learning techniques," Applied Sciences, vol. 15, no. 1, p. 118, 2025.
[6] S.A.Wani, S.A. Khan, G. Prashal, and D. Gupta. Smart Diagnosis of Incipient Faults Using Dissolved Gas Analysis-Based Fault Interpretation Matrix (FIM). Arab J Sci Eng, 44, 6977–6985 (2019).
[7] Y. Liu, B. Song, L. Wang, J. Gao, and R. Xu, “Power transformer fault diagnosis based on dissolved gas Analysis by Correlation Coefficient-DBSCAN,” Applied Sciences, vol. 10, no. 13, p. 4440, Jun. 2020.
[8] N. Suwarno, H. Sutikno, R. A. Prasojo, and A. Abu-Siada, “Machine learning based multi-method interpretation to enhance dissolved gas analysis for power transformer fault diagnosis,” Heliyon, vol. 10, no. 4, p. e25975, Feb. 2024.
[9] N. Manisha, K. Kaur, N. K. Sharma, J. Singh, and D. Bhalla, “Performance Assessment of IEEE/IEC Method and Duval Triangle technique for Transformer Incipient Fault Diagnosis,” IOP Conference Series Materials Science and Engineering, vol. 1228, no. 1, p. 012027, Mar. 2022.
[10] A. Abu-Siada, “Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming,” Energies, vol. 12, no. 4, p. 730, Feb. 2019.
[11] C. Guo, Q. Zhang, R. Zhang, X. He, Z. Wu, and T. Wen, “Investigation on gas generation characteristics in transformer oil under vibration,” IET Generation Transmission & Distribution, vol. 16, no. 24, pp. 5026–5040, Oct. 2022.
[12] G. K. Irungu, A. O. Akumu, and J. L. Munda, “A new fault diagnostic technique in oil-filled electrical equipment; the dual of Duval triangle,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 23, no. 6, pp. 3405–3410, Dec. 2016.
[13] A. Khanna and P. Bisht, “Rogers ratio test for fault diagnosis of transformer using dissolved gas analysis,” Materials Today Proceedings, vol. 71, pp. 243–246, Jan. 2022.
[14] J.-Y. Lim, D.-J. Lee, and P.-S. Ji, “Fault diagnosis of power transformer using confidence weight based fusion method,” 24th International Conference on Electrical Machines and Systems (ICEMS), vol. 71, pp. 1–4, Aug. 2017.
[15] E. Moradi, “A Data-Driven based Robust Multilayer Perceptron Approach for Fault Diagnosis of Power Transformers,” 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), Babol, Iran, Feb. 2024.
[16] A. Nanfak, A. Hechifa, S. Eke, A. Lakehal, C. H. Kom, and S. S. M. Ghoneim, “A combined technique for power transformer fault diagnosis based on k‐means clustering and support vector machine,” IET Nanodielectrics, vol. 7, no. 3, pp. 175–187, Jul. 2024.
[17] R. A. Prasojo, M. A. A. Putra, Ekojono, M. E. Apriyani, A. N. Rahmanto, S. S. M. Ghoneim, K. Mahmoud, M. Lehtonen, and M. M. F. Darwish, “Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique,” Electric Power Systems Research, vol. 220, p. 109361, Apr. 2023.
[18] A. Abdo, H. Liu, H. Zhang, J. Guo, and Q. Li, “A new model of faults classification in power transformers based on data optimization method,” Electric Power Systems Research, vol. 200, p. 107446, Jul. 2021.
[19] X. Lv, F. Liu, M. Jiang, F. Zhang, and L. Jia, “Fault diagnosis of power transformers based on dissolved gas analysis and improved LightGBM hybrid integrated model with dual‐branch structure,” IET Electric Power Applications, Dec. 2024.
[20] S. A. M. Abdelwahab, I. B. M. Taha, R. Fahim, and S. S. M. Ghoneim, "Transformer fault diagnose intelligent system based on DGA methods," Scientific Reports, vol. 15, p. 8263, 2025.
[21] A. Kirkbas, A. Demircali, S. Koroglu, and A. Kizilkaya, “Fault diagnosis of oil-immersed power transformers using common vector approach,” Electric Power Systems Research, vol. 184, p. 106346, Apr. 2020.
[22] D. Zou, Z. Li, H. Quan, Q. Peng, S. Wang, Z. Hong, W. Dai, T. Zhou, and J. Yin, “Transformer fault classification for diagnosis based on DGA and deep belief network,” Energy Reports, vol. 9, pp. 250–256, Oct. 2023.
[23] I. B. M. Taha, S. Ibrahim, and D.-E. A. Mansour, “Power transformer fault diagnosis based on DGA using a convolutional neural network with noise in measurements,” IEEE Access, vol. 9, pp. 111162–111170, Jan. 2021.
[24] L. Tightiz, M. A. Nasab, H. Yang, and A. Addeh, “An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis,” ISA Transactions, vol. 103, pp. 63–74, Mar. 2020.
[25] L. Wang, T. Littler, and X. Liu, “Dynamic incipient fault forecasting for power transformers using an LSTM model,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 30, no. 3, pp. 1353–1361, Mar. 2023.
[26] E. Moradi, "Accuracy enhancement of fault diagnosis for power transformers with a hybrid approach integrating robust and tree-based algorithms," Majlesi Journal of Electrical Engineering, vol. 19, no. 2, 2025.
[27] P. A. R. Azmi, M. Yusoff, and M. T. M. Sallehud-Din, “Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning,” Energy Reports, vol. 13, pp. 264–277, Dec. 2024.
[28] M. Demirci, H. Gözde, and M. C. Taplamacioglu, “Improvement of power transformer fault diagnosis by using sequential Kalman filter sensor fusion,” International Journal of Electrical Power & Energy Systems, vol. 149, p. 109038, Feb. 2023.
[29] J. Liu, Z. Zhao, Y. Zhong, C. Zhao, and G. Zhang, “Prediction of the dissolved gas concentration in power transformer oil based on SARIMA model,” Energy Reports, vol. 8, pp. 1360–1367, Mar. 2022.
[30] B. C. Mateus, J. T. Farinha, and M. Mendes, “Fault detection and prediction for power transformers using fuzzy logic and neural networks,” Energies, vol. 17, no. 2, p. 296, Jan. 2024.
[31] T. Kari, W. Gao, D. Zhao, K. Abiderexiti, W. Mo, Y. Wang, and L. Luan, “Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm,” IET Generation Transmission & Distribution, vol. 12, no. 21, pp. 5672–5680, Sep. 2018.
[32] S. A. Gamel, S. S. M. Ghoneim, and Y. A. Sultan, “Improving the accuracy of diagnostic predictions for power transformers by employing a hybrid approach combining SMOTE
and DNN,” Computers & Electrical Engineering, vol. 117, p. 109232, Apr. 2024.
[33] I. B. M. Taha and D.-E. A. Mansour, “Novel Power Transformer fault diagnosis using optimized machine learning methods,” Intelligent Automation & Soft Computing, vol. 28, no. 3, pp. 739–752, Jan. 2021.
[34] D. A. Mansour, "Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases," IEEE Trans. Dielectr. Electr. Insul., vol. 22, no. 5, pp. 2507-2512, Oct. 2015.
[35] G. Xu, M. Zhang, W. Chen, and Z. Wang, “Transformer fault diagnosis utilizing feature extraction and ensemble learning model,” Information, vol. 15, no. 9, p. 561, Sep. 2024.
[36] Ch. S. K. Dash, A. K. Behera, S. Dehuri, and A. Ghosh, “An outliers detection and elimination framework in classification task of data mining,” Decision Analytics Journal, vol. 6, p. 100164, Jan. 2023.
[37] C. Nkikabahizi, W. Cheruiyot, and A. Kibe, “Chaining Zscore and feature scaling methods to improve neural networks for classification,” Applied Soft Computing, vol. 123, p. 108908, May 2022.
[38] E. Moradi, “Comparative analysis of Tree-Based machine learning algorithms on thyroid disease prediction using ROS technique and hyperparameter optimization,” Journal of Artificial Intelligence and Data Mining, vol. 12, no. 4, pp. 511–520, 2024.
[39] A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Frontiers in Neurorobotics, vol. 7, Jan. 2013.
[40] J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.
[41] T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining (KDD), San Francisco, CA, USA, Aug. 2016, pp. 785–794.
[42] K. Budholiya, S. K. Shrivastava, and V. Sharma, “An optimized XGBoost based diagnostic system for effective prediction of heart disease,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 4514–4523, Oct. 2020.
[43] Y. Zhang, X. Li, and Z. Wang, "A histogram-based gradient boosting approach for predictive maintenance in industrial IoT systems," IEEE Trans. Ind. Informat., vol. 19, no. 3, pp. 2456–2465, Mar. 2023.
[44] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, "LightGBM: A highly efficient gradient boosting decision tree," in Proc. 31st Int. Conf. Neural Inf. Process. Syst. (NIPS), Long Beach, CA, USA, Dec. 2017, pp. 3149–3157.
[45] A. A. Yaghoubi, P. Karimi, E. Moradi, and R. Gavagsaz-Ghoachani, “Implementing engineering education based on posing a riddle in field of instrumentation and artificial intelligence”. 2023 9th International Conference on Control, Instrumentation and Automation (ICCIA), 2023, pp. 1–5.
[46] E. Moradi, " Leveraging Bayesian Optimization and Multilayer Artificial Neural Network (MLANN) for Fault Prediction in Oil-Immersed Transformers,"e-Prime- Advances in Electrical Engineering, Electronics and Energy, vol. 12, 2025.
[47] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proc. IEEE Int. Conf. Neural Networks, Perth, Australia, 1995, pp. 1942–1948.
[48] S. Rezashoar, and A. A. Rassafi, "Analyzing the Performance of the Red Deer Optimization Algorithm in Comparison to Other Metaheuristic Algorithms", Journal of AI and Data Mining, vol. 13, no. 1, pp. 53-61, 2025.
[49] M. Dehbozorgi, M, P. Shamsinejadbabaki, and E. Ashoormahani, "Better Neighbors, Longer Life: an Energy Efficient Cluster Head Selection Algorithm in Wireless Sensor Networks based on Particle Swarm Optimization", Journal of AI and Data Mining, vol. 11, no. 3, pp. 443-451, 2023.
[50] H. Kalani, and E. Abbasi, "Classification of sEMG Signals for Diagnosis of Unilateral Posterior Crossbite in Primary Dentition using Fast Fourier Transform and Logistic Regression", Journal of AI and Data Mining, vol. 10, no. 2, pp. 151-158, 2022.
[51] Z. Hassani, and M. Alambardar Meybodi, "Hybrid Particle Swarm Optimization with Ant-Lion Optimization: Experimental in Benchmarks and Applications", Journal of AI and Data Mining, vol. 9, no. 4, pp. 583-595, 2021.
[52] T. Sibalija, "Particle Swarm Optimisation in Designig Parameters of Manufacturing Processes: A Review (2008–2018) ". Applied Soft Computing, vol. 84, pp. 1-33, 2019.
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