Dissolved Gas Analysis of Insulating Oil for Power Transformer Fault Diagnosis with Bayesian Neural Network
Main Article Content
Abstract
Dissolved gas analysis (DGA) is widely used for preventative maintenance techniques and fault diagnoses of oil-immersed power transformers. There are also various conventional methods of DGA for insulating oil in power transformers including methods of Doernenburg ratios, Rogers ratios and Duval’s triangle. The Bayesian techniques have been developed over many years in a range of different fields and have been also applied to the problem of training in artificial neural networks (ANNs). In particular, the Bayesian approach can solve the problem of over-fitting of ANNs after being trained. The Bayesian framework can be also utilized to compare and rank different architectures and types of ANNs. This research aims at deploying a detailed procedure of training ANNs with the Bayesian inference, also known as Bayesian neural networks (BNNs), to classify power transformer faults based on Doernenburg and Rogers gas ratios. In this research, the IEC TC 10 database was used to form training and testing data sets. The results obtained from the performance of trained BNNs show that despite the limitation of the available DGA data, BNNs with an appropriate number of hidden units can successfully classify power transformer faults with accuracy rates greater than 80%.
Keywords
Power transformers, fault diagnosis, dissolved gas analysis, Bayesian neural networks
Article Details
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References
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[3] Osama E. Gouda, Salah Hamdy El-Hoshy, Sherif S. M. Ghoneim, Enhancing the diagnostic accuracy of DGA techniques based on IEC-TC10 and related databases, IEEE Access, vol. 9, pp. 118031–118041, Aug. 2021. https://doi.org/10.1109/ACCESS.2021.3107332
[4] Ibrahim B. M. Taha, Hatim G. Zaini, Sherif. S. M. Ghoneim, Comparative study between Dorneneburg and Rogers methods for transformer fault diagnosis based on dissolved gas analysis using Matlab Simulink Tools, 2015 IEEE Conference on Energy Conversion (CENCON), 2015, pp. 363–367.
[5] Jawad Faiz, Milad Soleimani, Assessment of computational intelligence and conventional dissolved gas analysis methods for transformer fault diagnosis, IEEE Trans. Dielectrics and Electrical Insulation, vol. 25, no. 5, pp. 1798–1806, Oct. 2018. https://doi.org/10.1109/TDEI.2018.0071915
[6] J.L. Guardado, J.L. Naredo, P. Moreno, C.R. Fuerte, A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis, IEEE Transactions on Power Delivery, vol. 16, no. 4, pp. 643–647, Oct. 2001. https://doi.org/10.1109/61.956751
[7] Jiejie Dai, Hui Song, Gehao Sheng, Xiuchen Jiang, Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network, IEEE Trans. Dielectrics and Electrical Insulation, vol. 24, no. 5, pp. 2828–2835, Oct. 2017. https://doi.org/10.1109/TDEI.2017.006727
[8] Q. Su, C. Mi, L.L. Lai, P. Austin, A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer, IEEE Trans. Power Systems, vol. 15, no. 2, pp. 593–598, May. 2000. https://doi.org/10.1109/59.867146
[9] Secil Genc, Serap Karagol, Fuzzy logic application in DGA methods to classify fault type in power transformer, 2020 International Congress on Human–Computer Interaction, Optimization and Robotic Applications (HORA), 2020. https://doi.org/10.1109/HORA49412.2020.9152896
[10] Seifeddine Souahlia, Khmais Bacha, Abdelkader Chaari, SVM-based decision for power transformers fault diagnosis using Rogers and Doernenburg ratios DGA, 10th International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13), 2013, pp. 1–6. https://doi.org/10.1109/SSD.2013.6564073
[11] Yuhan Wu, Xianbo Sun, Yi Zhang, Xianjing Zhong, Lei Cheng, A power transformer fault diagnosis method-based hybrid improved seagull optimization algorithm and support vector machine, IEEE Access, vol. 10, pp. 17268–17286, Nov. 2021. https://doi.org/10.1109/ACCESS.2021.3127164
[12] Arief Basuki, Suwarno, Online dissolved gas analysis of power transformers based on decision tree model, 2018 Conference on Power Engineering and Renewable Energy (ICPERE), 2018. https://doi.org/10.1109/ICPERE.2018.8739761
[13] Omar Kherif, Youcef Benmahamed, Madjid Teguar, Ahmed Boubakeur, Sherif S. M. Ghoneim, Accuracy improvement of power transformer faults diagnostic using KNN classifier with decision tree principle, IEEE Access, 2021, pp. 81693–81701. https://doi.org/10.1109/ACCESS.2021.3086135
[14] Y. Benmahamed, Y. Kemari, M. Teguar, A. Boubakeur, Diagnosis of power transformer oil using KNN and naive Bayes classifiers, 2018 IEEE 2nd International Conference on Dielectrics (ICD), 2018. https://doi.org/10.1109/ICD.2018.8514789
[15] Wenxiong Mo, Tusongjiang Kari, Hongbing Wang, Le Luan, Wensheng Gao, Fault diagnosis of power transformer using feature selection techniques and KNN, 2017 3rd IEEE International Conference on Computer and Communications (ICCC), 2017, pp. 2827–2831.
[16] D. Mackay, A practical Bayesian framework for backpropagation networks, Computation and Neural Systems, vol. 4, pp. 448–472, 1992. https://doi.org/10.1162/neco.1992.4.3.448
[17] Ian T. Nabney, Netlab: Algorithms for pattern recognition, Advances in Pattern Recognition, Springer, 2001.
[18] W.D. Penny, S.J. Robert, Bayesian neural networks for Classification: how useful is the evidence framework, Neural Networks, vol. 12, pp. 877–892, 1999. https://doi.org/10.1016/S0893-6080(99)00040-4