Subject Areas : International Journal of Smart Electrical Engineering
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Keywords:
Abstract :
References:
[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] 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), Feb. 2024.
[3] M. M. Islam, G. Lee, and S. N. Hettiwatte, “Application of a general regression neural network for health index calculation of power transformers,” International Journal of Electrical Power & Energy Systems, vol. 93, pp. 308–315, 2017.
[4] D.-E. A. Mansour, “Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 22, no. 5, 2015.
[5] Y. Benmahamed, M. Teguar, and A. Boubakeur, “Application of SVM and KNN to Duval Pentagon 1 for transformer oil diagnosis,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, no. 6, 2017.
[6] A.-M. Aciu, S. Enache, and M.-C. Nițu, “A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis,” Energies, vol. 17, no. 10, May 2024.
[7] R. Soni and B. Mehta, “Diagnosis and prognosis of incipient faults and insulation status for asset management of power transformer using fuzzy logic controller & fuzzy clustering means,” Electric Power Systems Research, vol. 220, 2023.
[8] M. S. Ali, A. H. A. Bakar, A. Omar, A. S. A. Jaafar, and S. H. Mohamed, “Conventional methods of dissolved gas analysis using oil-immersed power transformer for fault diagnosis: A review,” Electric Power Systems Research, vol. 216, 2023.
[9] Y. Jin, H. Wu, J. Zheng, J. Zhang, and Z. Liu, “Power Transformer Fault Diagnosis Based on Improved BP Neural Network,” Electronics, vol. 12, no. 16, 2023.
[10] Y. Zhang, Y. Tang, Y. Liu, and Z. Liang, “Fault diagnosis of transformer using artificial intelligence: A review,” Frontiers in Energy Research, vol. 10, 2022.
[11] 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, 2024.
[12] L. Wang, T. Littler, and X. Liu, “Hybrid AI model for power transformer assessment using imbalanced DGA datasets,” IET Renewable Power Generation, vol. 17, no. 8, 2023.
[13] 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, 2020.
[14] D. Zou et al., “Transformer fault classification for diagnosis based on DGA and deep belief network,” Energy Reports, vol. 9, pp. 250–256, 2023.
[15] N. Poonnoy, C. Suwanasri, and T. Suwanasri, “Fuzzy Logic Approach to Dissolved Gas Analysis for Power Transformer Failure Index and Fault Identification,” Energies, vol. 14, no. 1, 2020.
[16] 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, 2021.
[17] S. S. M. Ghoneim and I. B. M. Taha, “A new approach of DGA interpretation technique for transformer fault diagnosis,” International Journal of Electrical Power & Energy Systems, vol. 81, pp. 265–274, Oct. 2016.
[18] V. N. G. Raju, K. P. Lakshmi, V. M. Jain, A. Kalidindi, and V. Padma, “Study the Influence of Normalization/Transformation Process on the Accuracy of Supervised Classification,” 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Aug. 2020.
[19] K. Bacha, S. Souahlia, and M. Gossa, “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine,” Electric Power Systems Research, vol. 83, no. 1, pp. 73–79, 2012.
[20] Y. Y. Song, and Y. Lu, “Decision tree methods: applications for classification and prediction”, Shanghai Archives of Psychiatry, vol. 27, no. 2, 2015.
[21] Y. D. Almoallem, I. B. M. Taha, M. I. Mosaad, L. Nahma, and A. Abu-Siada, “Application of Logistic Regression Algorithm in the Interpretation of Dissolved Gas Analysis for Power Transformers,” Electronics, vol. 10, no. 10, 2021.
[22] 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, 2023.
[23] 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,” 9th International Conference on Control, Instrumentation and Automation (ICCIA), Dec. 2023.
[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] 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), Feb. 2024.
[3] M. M. Islam, G. Lee, and S. N. Hettiwatte, “Application of a general regression neural network for health index calculation of power transformers,” International Journal of Electrical Power & Energy Systems, vol. 93, pp. 308–315, 2017.
[4] D.-E. A. Mansour, “Development of a new graphical technique for dissolved gas analysis in power transformers based on the five combustible gases,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 22, no. 5, 2015.
[5] Y. Benmahamed, M. Teguar, and A. Boubakeur, “Application of SVM and KNN to Duval Pentagon 1 for transformer oil diagnosis,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 24, no. 6, 2017.
[6] A.-M. Aciu, S. Enache, and M.-C. Nițu, “A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis,” Energies, vol. 17, no. 10, May 2024.
[7] R. Soni and B. Mehta, “Diagnosis and prognosis of incipient faults and insulation status for asset management of power transformer using fuzzy logic controller & fuzzy clustering means,” Electric Power Systems Research, vol. 220, 2023.
[8] M. S. Ali, A. H. A. Bakar, A. Omar, A. S. A. Jaafar, and S. H. Mohamed, “Conventional methods of dissolved gas analysis using oil-immersed power transformer for fault diagnosis: A review,” Electric Power Systems Research, vol. 216, 2023.
[9] Y. Jin, H. Wu, J. Zheng, J. Zhang, and Z. Liu, “Power Transformer Fault Diagnosis Based on Improved BP Neural Network,” Electronics, vol. 12, no. 16, 2023.
[10] Y. Zhang, Y. Tang, Y. Liu, and Z. Liang, “Fault diagnosis of transformer using artificial intelligence: A review,” Frontiers in Energy Research, vol. 10, 2022.
[11] 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, 2024.
[12] L. Wang, T. Littler, and X. Liu, “Hybrid AI model for power transformer assessment using imbalanced DGA datasets,” IET Renewable Power Generation, vol. 17, no. 8, 2023.
[13] 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, 2020.
[14] D. Zou et al., “Transformer fault classification for diagnosis based on DGA and deep belief network,” Energy Reports, vol. 9, pp. 250–256, 2023.
[15] N. Poonnoy, C. Suwanasri, and T. Suwanasri, “Fuzzy Logic Approach to Dissolved Gas Analysis for Power Transformer Failure Index and Fault Identification,” Energies, vol. 14, no. 1, 2020.
[16] 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, 2021.
[17] S. S. M. Ghoneim and I. B. M. Taha, “A new approach of DGA interpretation technique for transformer fault diagnosis,” International Journal of Electrical Power & Energy Systems, vol. 81, pp. 265–274, Oct. 2016.
[18] V. N. G. Raju, K. P. Lakshmi, V. M. Jain, A. Kalidindi, and V. Padma, “Study the Influence of Normalization/Transformation Process on the Accuracy of Supervised Classification,” 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), Aug. 2020.
[19] K. Bacha, S. Souahlia, and M. Gossa, “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine,” Electric Power Systems Research, vol. 83, no. 1, pp. 73–79, 2012.
[20] Y. Y. Song, and Y. Lu, “Decision tree methods: applications for classification and prediction”, Shanghai Archives of Psychiatry, vol. 27, no. 2, 2015.
[21] Y. D. Almoallem, I. B. M. Taha, M. I. Mosaad, L. Nahma, and A. Abu-Siada, “Application of Logistic Regression Algorithm in the Interpretation of Dissolved Gas Analysis for Power Transformers,” Electronics, vol. 10, no. 10, 2021.
[22] 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, 2023.
[23] 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,” 9th International Conference on Control, Instrumentation and Automation (ICCIA), Dec. 2023.
