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A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis

Year 2025, Volume: 4 Issue: 2, 449 - 497, 25.07.2025

Mustafa Aljammal , Omar Al-yozbaky

Abstract

For electrical power systems to remain stable, power transformers must operate dependably. If left unnoticed or identified too late, transformer defects can result in expensive downtime and catastrophic breakdowns. Recent developments in intelligent models for transformer failure monitoring and diagnosis are examined in this cutting-edge review. It looks at how machine learning, deep learning, and expert systems can be combined to detect defects such temperature anomalies, winding deformation, and partial discharges. Examined are the efficacy and scalability of significant methodologies, including hybrid algorithms, data-driven tactics, and Internet of Things (IoT) technologies. The analysis also highlights current constraints, including as data availability and model interpretability, and suggests future research goals to enhance transformer reliability and predictive maintenance practices. This paper aims to provide researchers and practitioners with a comprehensive grasp of intelligent monitoring systems to transform transformer fault management. This work opens up a wide range of possibilities for creating intelligent models that are more accurate and efficient, improving the models' capacity to handle inconsistent or incomplete data, successfully integrating Internet of Things technologies, making models easier to understand, and creating predictive maintenance systems that work. This significantly affects the longevity of transformers, lowers maintenance costs, and boosts electrical power grid dependability.

Keywords

Transformer Finite Elements Method Fault diagnosis IoT

References

  • [1] Date, S. & Date, A.,. “Evaluation of Renewable Energy Alternative for Ankara and Selection of Appropriate Power Plant Location Furkan Algül 1* , Müşerref Nur Koruk 2 Babek Erdebilli”. 2: 357–362, (2023)
  • [2] Najem, W. M. & Khudher, S. M.,. “An Intelligent Method to Adapt the Distance Relay in Power System Fault Detection with Electric Vehicles Presence”. 2: 253–262, (2023)
  • [3] Xie, B., Zhao, D. & Hong, T.,. “Transformer Monitoring and Protection in Dynamic Power Systems – A Review”. , Frontiers in Energy Research , 8: 1–15, (2020)
  • [4] Metwally, I. A.,. “Failures, monitoring and new trends of power transformers”. , IEEE Potentials , 30: 36–43, (2011)
  • [5] Murugan, R. & Ramasamy, R.,. “Understanding the power transformer component failures for health index-based maintenance planning in electric utilities”. , Engineering Failure Analysis , 96: 274–288, (2019)
  • [6] Dumitran, L. M., Setnescu, R., Notingher, P. V., Badicu, L. V. & Setnescu, T.,. “Method for lifetime estimation of power transformer mineral oil”. , Fuel , 117: 756–762, (2014)
  • [7] Mahmood, M. H. & Makki, S. A.,. “Power Distribution Transformers Monitoring Based on Zigbee and Sensors Technology”. , Proceedings of the 7th International Engineering Conference ‘Research and Innovation Amid Global Pandemic’, IEC 2021 , 112–117, (2021) doi:10.1109/IEC52205.2021.9476136
  • [8] Madavan, R. & Balaraman, S.,. “Failure analysis of transformer liquid - solid insulation system under selective environmental conditions using Weibull statistics method”. , Engineering Failure Analysis , 65: 26–38, (2016)
  • [9] Rokani, V., Kaminaris, S. D., Karaisas, P. & Kaminaris, D.,. “Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques”. , Mathematics , 11: (2023)
  • [10] Roland, U. & Eseosa, O.,. “Artificial Neural Network Approach to Distribution Transformers Maintenance”. , International Journal of Scientific Research & Engineering Trends , 1: 2395–566, (2015) [11] Bhalla, D., Bansal, R. K. & Gupta, H. O.,. “Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis”. , International Journal of Electrical Power and Energy Systems , 43: 1196–1203, (2012) [12] Zhang, K., Yuan, F., Guo, J. & Wang, G.,. “A Novel Neural Network Approach to Transformer Fault Diagnosis Based on Momentum-Embedded BP Neural Network Optimized by Genetic Algorithm and Fuzzy c-Means”. , Arabian Journal for Science and Engineering , 41: 3451–3461,
  • [13] Nagpal, T. & Brar, Y. S.,. “Neural network based transformer incipient fault detection”. , 2014 International Conference on Advances in Electrical Engineering, ICAEE 2014 , (2014) doi:10.1109/ICAEE.2014.6838535 [14] Bacha, K., Souahlia, S. & Gossa, M.,. “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine”. , Electric Power Systems Research , 83: 73–79, (2012)
  • [15] Kari, T. et al.,. “Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm”. , IET Generation, Transmission and Distribution , 12: 5672–5680, (2018)
  • [16] Li, J. et al.,. “Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine”. , IEEE Transactions on Dielectrics and Electrical Insulation , 23: 1198–1206, (2016)
  • [17] Zhang, Y. et al.,. “A Fault Diagnosis Model of Power Transformers Based on Dissolved Gas Analysis Features Selection and Improved Krill Herd Algorithm Optimized Support Vector Machine”. , IEEE Access , 7: 102803–102811, (2019)
  • [18] Zheng, H. B., Liao, R. J., Grzybowski, S. & Yang, L. J.,. “Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation”. , IET Electric Power Applications , 5: 691–696, (2011)
  • [19] Liu, J. et al.,. “Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine”. , IEEE Access , 7: 112494–112504, (2019)
  • [20] Husain, Z.,. “Fuzzy logic expert system for incipient fault diagnosis of power transformers”. , International Journal on Electrical Engineering and Informatics , 10: 300–317, (2018)
  • [21] Ranga, C., Chandel, A. K. & Chandel, R.,. “Expert system for condition monitoring of power transformer using fuzzy logic”. , Journal of Renewable and Sustainable Energy , 9: (2017)
  • [22] Ranga, C., Chandel, A. K. & Chandel, R.,. “Fuzzy logic expert system for optimum maintenance of power transformers”. , International Journal on Electrical Engineering and Informatics , 8: 835–849, (2016)
  • [23] Khlebtsov, A. P., Shilin, A. N., Rybakov, A. V. & Klyucharev, A. Y.,. “Development of a fuzzy expert system for power transformer diagnostics”. , Journal of Physics: Conference Series , 2091: (2021)
  • [24] Velasquez, R. M. A. & Lara, J. V. M.,. “Expert system for power transformer diagnosis”. , Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 , (2017) doi:10.1109/INTERCON.2017.8079640
  • [25] Qiu, S. et al.,. “Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review”. , Sensors , 23: (2023)
  • [26] Zhang, C. et al.,. “Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning”. , Future Generation Computer Systems , 108: 533–545, (2020)
  • [27] Hong, K., Jin, M. & Huang, H.,. “Transformer winding fault diagnosis using vibration image and deep learning”. , IEEE Transactions on Power Delivery , 36: 676–685, (2021)
  • [28] Elsisi, M. et al.,. “Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties”. , Measurement: Journal of the International Measurement Confederation , 190: (2022)
  • [29] Jin, Y., Hou, L. & Chen, Y.,. “A Time Series Transformer based method for the rotating machinery fault diagnosis”. , Neurocomputing , 494: 379–395, (2022)
  • [30] Pawar, R. R. & Deosarkar, S. B.,. “Health condition monitoring system for distribution transformer using Internet of Things (IoT)”. , Proceedings of the International Conference on Computing Methodologies and Communication, ICCMC 2017 , 2018–Janua: 117–122, (2017)
  • [31] Cheng, X. H. & Wang, Y.,. “The remote monitoring system of transformer fault based on the internet of things”. , Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 , 1: 84–87, (2011)
  • [32] Al Mhdawi, A. K. & Al-Raweshidy, H. S.,. “A Smart Optimization of Fault Diagnosis in Electrical Grid Using Distributed Software-Defined IoT System”. , IEEE Systems Journal , 14: 2780–2790, (2020)
  • [33] Chi, Y., Dong, Y., Wang, Z. J., Yu, F. R. & Leung, V. C. M.,. “Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A Survey”. , IEEE Internet of Things Journal , 9: 12886–12900, (2022)
  • [34] Mohamad, A. A. H., Mezaal, Y. S. & Abdulkareem, S. F.,. “Computerized power transformer monitoring based on internet of things”. , International Journal of Engineering and Technology(UAE) , 7: 2773–2778, (2018)
  • [35] Wu, Y., Sun, X., Zhang, Y., Zhong, X. & Cheng, L.,. “A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine”. , IEEE Access , 10: 17268–17286, (2022)
  • [36] Hoballah, A., Mansour, D. E. A. & Taha, I. B. M.,. “Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements”. , IEEE Access , 8: 139176–139187, (2020)
  • [37] Fan, J. et al.,. “Hybrid RVM-ANFIS algorithm for transformer fault diagnosis”. , IET Generation, Transmission and Distribution , 11: 3637–3643, (2017)
  • [38] Fang, J. et al.,. “A transformer fault diagnosis model using an optimal hybrid dissolved gas analysis features subset with improved social group optimization-support vector machine classifier”. , Energies , 11: (2018)
  • [39] Al-Janabi, S., Rawat, S., Patel, A. & Al-Shourbaji, I.,. “Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers”. , International Journal of Electrical Power and Energy Systems , 67: 324–335, (2015)
  • [40] Illias, H. A., Chai, X. R. & Abu Bakar, A. H.,. “Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis”. , Measurement: Journal of the International Measurement Confederation , 90: 94–102, (2016)
  • [41] El-kenawy, E. S. M. et al.,. “Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm”. , Mathematics , 10: (2022)
  • [42] Wong, S. Y., Ye, X., Guo, F. & Goh, H. H.,. “Computational intelligence for preventive maintenance of power transformers”. , Applied Soft Computing , 114: 108129, (2022)
  • [43] Li, D., Wang, B. & Feng, J.,. “Simulation Analysis of Power Transformer Internal Short Circuit for Magnetic Flux Leakage Distribution Feature Extraction”. , Proceedings of 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023 , (2023) doi:10.1109/SAFEPROCESS58597.2023.10295835
  • [44] Chothani, N. & Patel, D.,. “Modern Method to Discriminate High Resistance Internal Winding Faults and Inrush Condition/External Faults in Power Transformer”. , 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024 , (2024) doi:10.1109/ODICON62106.2024.10797598
  • [45] Abdusalam, O., Anayi, F. & Packianather, M.,. “Discrimination between internal current and external fault in three phase power transformer by Using alienation coefficient”. , 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings , (2022) doi:10.1109/UPEC55022.2022.9917688
  • [46] Li, Y., Fang, J., Zhang, Y. & Xu, Y.,. “Analysis of Typical Cases of Transformer Internal Faults”. , 2022 2nd International Conference on Electrical Engineering and Control Science, IC2ECS 2022 , 1022–1025, (2022) doi:10.1109/IC2ECS57645.2022.10088112
  • [47] Low, S. H.,. “Three-phase Transformer Modeling”. , 2023 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023 , (2023) doi:10.1109/Allerton58177.2023.10313400
  • [48] Pani, S. R., Bera, P. K. & Kumar, V.,. “Detection and classification of internal faults in power transformers using tree based classifiers”. , 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020 , (2020) doi:10.1109/PEDES49360.2020.9379641
  • [49] Bera, P. K., Isik, C. & Kumar, V.,. “Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators”. , IEEE Systems Journal , 15: 3450–3461, (2021)
  • [50] Xiu, Z. et al.,. “Analysis of an Internal Fault of Main Transformer”. , Lecture Notes in Electrical Engineering , 1054 LNEE: 767–776, (2023)
  • [51] Ahmadi, H., Vahidi, B. & Foroughi Nematollahi, A.,. “A simple method to detect internal and external short-circuit faults, classify and locate different internal faults in transformers”. , Electrical Engineering , 103: 825–836, (2021)
  • [52] Abu-Siada, A.,. “Improved consistent interpretation approach of fault type within power transformers using dissolved gas analysis and gene expression programming”. , Energies , 12: (2019)
  • [53] Abd El-Aal, R. A., Helal, K., Hassan, A. M. M. & Dessouky, S. S.,. “Prediction of Transformers Conditions and Lifetime Using Furan Compounds Analysis”. , IEEE Access , 7: 102264–102273, (2019)
  • [54] Wu, Y., Sun, X., Yang, P. & Wang, Z.,. “Transformer fault diagnosis based on Improved Particle Swarm Optimization to support Vector Machine”. , Journal of Physics: Conference Series , 1750: (2021)
  • [55] Al-Yozbaky, O. S. & Othman, R. A.,. “The Influence of Non-Sinusoidal Power Supply on Single-Phase Transformer Performance”. , Journal Européen des Systèmes Automatisés , 57: 1015, (2024)
  • [56] Landaburu, J.,. “INVESTIGATING SOME IMPORTANT PARAMETERS OF THE PDC MEASUREMENT TECHNIQUE FOR THE INSULATION CONDITION ASESSMENT OF POWER TRANSFORMER”. 1–23, (2016)
  • [57] Bai, P. & Jia, Y.,. “Simulation analysis of internal and external faults in transformer area”. , MATEC Web of Conferences , 382: 01041, (2023)
  • [58] Khalyasmaa, A. I., Senyuk, M. D. & Eroshenko, S. A.,. “High-Voltage Circuit Breakers Techn[1] S. Y. Wong, X. Ye, F. Guo, and H. H. Goh, ‘Computational intelligence for preventive maintenance of power transformers,’ Appl. Soft Comput., vol. 114, p. 108129, 2022, doi: 10.1016/j.asoc.2021.108129.i[1] S. Y. Wong,”. , IEEE Transactions on Power Delivery , 34: 1747–1756, (2019)
  • [59] Zhao, Z. et al.,. “Diagnosing Transformer Winding Deformation Faults Based on the Analysis of Binary Image Obtained from FRA Signature”. , IEEE Access , 7: 40463–40474, (2019)
  • [60] Kari, T., Gao, W., Tuluhong, A., Yaermaimaiti, Y. & Zhang, Z.,. “Mixed kernel function support vector regression with genetic algorithm for forecasting dissolved gas content in power transformers”. , Energies , 11: (2018)
  • [61] Lee, J. S., Seo, W. S. & Kang, S. H.,. “An Internal Fault Model of a Transformer Bank Using the UDM of Matlab/Simulink”. , 2023 International Conference on Advanced Power System Automation and Protection, APAP 2023 , 50–55, (2023) doi:10.1109/APAP59666.2023.10348495
  • [62] Ramesh, J., Shahriar, S., Al-Ali, A. R., Osman, A. & Shaaban, M. F.,. “Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System”. , Energies , 15: 1–19, (2022) [63] ÖZÜPAK, Y.,. “Performing Structural Design and Modeling of Transformers Using ANSYS-Maxwell”. , Brilliant Engineering , 2: 38–42, (2021)
  • [64] Bacha, K., Souahlia, S. & Gossa, M.,. “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine”. , Electric Power Systems Research , 83: 73–79, (2012) [65] Tenbohlen, S., Jagers, J. & Vahidi, F.,. “Standardized Survey of Transformer Reliability”. , Conference Proceedings of ISEIM , 2008–2011, (2017)
  • [66] Bai, C. F., Gao, W. S. & Liu, T.,. “Analyzing the impact of ambient temperature indicators on transformer life in different regions of Chinese mainland”. , The Scientific World Journal , 2013: (2013)
  • [67] Eslami, A.,. “A three-phase comprehensive methodology to analyze short circuits, open circuits and internal faults of transformers based on the comp ensation theorem”. , International Journal of Electrical Power and Energy Systems , 96: 238–252, (2018)
  • [68] Thangavelan, M., Prabavathi, K. & Ramesh, L.,. “Review on power transformer internal fault diagnosis”. , Journal of Electrical Engineering , 14: 1–7, (2014)
  • [69] Asadi, N. & Kelk, H. M.,. “Modeling, Analysis, and Detection of Internal Winding Faults in Power Transformers”. , IEEE Transactions on Power Delivery , 30: 2419–2426, (2015)
  • [70] Etumi, A. A. A. & Anayi, F. J.,. “The application of correlation technique in detecting internal and external faults in three-phase transformer and saturation of current transformer”. , IEEE Transactions on Power Delivery , 31: 2131–2139, (2016)
  • [71] Tenbohlen, S. et al.,. “Diagnostic measurements for power transformers”. , Energies , 9: (2016)
  • [72] Alkahdely, S. M. A. & Alsammak, A. N. B.,. “Normal operation and reverse action of on-load tap changing transformer with its effect on voltage stability”. , Bulletin of Electrical Engineering and Informatics , 12: 650–658, (2023)
  • [73] Alkahdely, S. M. A. & Alsammak, A. N. B.,. “Non-Limiting Operation of The On-Load Tap Changing Transformer, and Its Effect on Voltage Stability, with Regards to The Nineveh Electrical Grid”. , Przeglad Elektrotechniczny , 99: 181–186, (2023)
  • [74] Koch, M. & Prevost, T.,. “Analysis of dielectric response measurements for condition assessment of oil-paper transformer insulation”. , IEEE Transactions on Dielectrics and Electrical Insulation , 19: 1908–1915, (2012)
  • [75] Zuo, W., Yuan, H., Shang, Y., Liu, Y. & Chen, T.,. “Calculation of a health index of oil-paper transformers insulation with binary logistic regression”. , Mathematical Problems in Engineering , 2016: (2016)
  • [76] Martin, D., Cui, Y., Ekanayake, C., Hui, M. & Saha, T.,. “An updated model to determine the life remaining of transformer insulation”. , IEEE Transactions on Power Delivery , 30: 395–402, (2015)
  • [77] Norazhar, A. B., Abu-Siada, A. & Islam, S.,. “A review on chemical diagnosis techniques for transformer paper insulation degradation”. , 2013 Australasian Universities Power Engineering Conference, AUPEC 2013 , (2013) doi:10.1109/aupec.2013.6725476
  • [78] Alqudsi, A. Y. & Elhag, A. H.,. “A cost effective artificial intelligence based transformer insulation health index”. , 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems, CATCON 2017 - Proceedings , 2018–Janua: 108–111, (2017)
  • [79] Alyozbaky, O. S., Ab Kadir, M. Z. A. A., Izadi, M., Gomes, C. & Azis, N. B.,. “The behaviour of three phase three-leg 11KV transformer core type design under sinusoidal and non-sinusoidal operating conditions for different core materials”. , International Journal of Simulation: Systems, Science and Technology , 17: 27.1-27.8, (2017)
  • [80] Wouters, P. A. A. F., Van Schijndel, A. & Wetzer, J. M.,. “Remaining lifetime modeling of power transformers: Individual assets and fleets”. , IEEE Electrical Insulation Magazine , 27: 45–51, (2011)
  • [81] Alyozbaky, O. S.,. “The losses and temperature comparison in three-phase distribution transformer with various assembly core designs”. , Australian Journal of Electrical and Electronics Engineering , 15: 61–70, (2018)
  • [82] Oliveira, L. S. M. R. & Cardoso, A. J. M.,. “A permeance-based transformer model and its application to winding interturn arcing fault studies”. , IEEE Transactions on Power Delivery , 25: 1589–1598, (2010)
  • [83] Sun, C., Ohodnicki, P. R. & Stewart, E. M.,. “Chemical Sensing Strategies for Real-Time Monitoring of Transformer Oil: A Review”. , IEEE Sensors Journal , 17: 5786–5806, (2017)
  • [84] De Faria, H., Costa, J. G. S. & Olivas, J. L. M.,. “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis”. , Renewable and Sustainable Energy Reviews , 46: 201–209, (2015)
  • [85] Cheng, L. & Yu, T.,. “Dissolved gas analysis principle-based intelligent approaches to fault diagnosis and decision making for large oil-immersed power transformers: A survey”. , Energies , 11: 1–69, (2018)
  • [86] Khalili Senobari, R., Sadeh, J. & Borsi, H.,. “Frequency response analysis (FRA) of transformers as a tool for fault detection and location: A review”. , Electric Power Systems Research , 155: 172–183, (2018)
  • [87] Gomez-Luna, E., Aponte Mayor, G., Gonzalez-Garcia, C. & Pleite Guerra, J.,. “Current status and future trends in frequency-response analysis with a transformer in service”. , IEEE Transactions on Power Delivery , 28: 1024–1031, (2013)
  • [88] Yao, C. et al.,. “Transformer winding deformation diagnostic system using online high frequency signal injection by capacitive coupling”. , IEEE Transactions on Dielectrics and Electrical Aljammal,

Year 2025, Volume: 4 Issue: 2, 449 - 497, 25.07.2025

Mustafa Aljammal , Omar Al-yozbaky

Abstract

References

  • [1] Date, S. & Date, A.,. “Evaluation of Renewable Energy Alternative for Ankara and Selection of Appropriate Power Plant Location Furkan Algül 1* , Müşerref Nur Koruk 2 Babek Erdebilli”. 2: 357–362, (2023)
  • [2] Najem, W. M. & Khudher, S. M.,. “An Intelligent Method to Adapt the Distance Relay in Power System Fault Detection with Electric Vehicles Presence”. 2: 253–262, (2023)
  • [3] Xie, B., Zhao, D. & Hong, T.,. “Transformer Monitoring and Protection in Dynamic Power Systems – A Review”. , Frontiers in Energy Research , 8: 1–15, (2020)
  • [4] Metwally, I. A.,. “Failures, monitoring and new trends of power transformers”. , IEEE Potentials , 30: 36–43, (2011)
  • [5] Murugan, R. & Ramasamy, R.,. “Understanding the power transformer component failures for health index-based maintenance planning in electric utilities”. , Engineering Failure Analysis , 96: 274–288, (2019)
  • [6] Dumitran, L. M., Setnescu, R., Notingher, P. V., Badicu, L. V. & Setnescu, T.,. “Method for lifetime estimation of power transformer mineral oil”. , Fuel , 117: 756–762, (2014)
  • [7] Mahmood, M. H. & Makki, S. A.,. “Power Distribution Transformers Monitoring Based on Zigbee and Sensors Technology”. , Proceedings of the 7th International Engineering Conference ‘Research and Innovation Amid Global Pandemic’, IEC 2021 , 112–117, (2021) doi:10.1109/IEC52205.2021.9476136
  • [8] Madavan, R. & Balaraman, S.,. “Failure analysis of transformer liquid - solid insulation system under selective environmental conditions using Weibull statistics method”. , Engineering Failure Analysis , 65: 26–38, (2016)
  • [9] Rokani, V., Kaminaris, S. D., Karaisas, P. & Kaminaris, D.,. “Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques”. , Mathematics , 11: (2023)
  • [10] Roland, U. & Eseosa, O.,. “Artificial Neural Network Approach to Distribution Transformers Maintenance”. , International Journal of Scientific Research & Engineering Trends , 1: 2395–566, (2015) [11] Bhalla, D., Bansal, R. K. & Gupta, H. O.,. “Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis”. , International Journal of Electrical Power and Energy Systems , 43: 1196–1203, (2012) [12] Zhang, K., Yuan, F., Guo, J. & Wang, G.,. “A Novel Neural Network Approach to Transformer Fault Diagnosis Based on Momentum-Embedded BP Neural Network Optimized by Genetic Algorithm and Fuzzy c-Means”. , Arabian Journal for Science and Engineering , 41: 3451–3461,
  • [13] Nagpal, T. & Brar, Y. S.,. “Neural network based transformer incipient fault detection”. , 2014 International Conference on Advances in Electrical Engineering, ICAEE 2014 , (2014) doi:10.1109/ICAEE.2014.6838535 [14] Bacha, K., Souahlia, S. & Gossa, M.,. “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine”. , Electric Power Systems Research , 83: 73–79, (2012)
  • [15] Kari, T. et al.,. “Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm”. , IET Generation, Transmission and Distribution , 12: 5672–5680, (2018)
  • [16] Li, J. et al.,. “Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine”. , IEEE Transactions on Dielectrics and Electrical Insulation , 23: 1198–1206, (2016)
  • [17] Zhang, Y. et al.,. “A Fault Diagnosis Model of Power Transformers Based on Dissolved Gas Analysis Features Selection and Improved Krill Herd Algorithm Optimized Support Vector Machine”. , IEEE Access , 7: 102803–102811, (2019)
  • [18] Zheng, H. B., Liao, R. J., Grzybowski, S. & Yang, L. J.,. “Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimisation”. , IET Electric Power Applications , 5: 691–696, (2011)
  • [19] Liu, J. et al.,. “Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine”. , IEEE Access , 7: 112494–112504, (2019)
  • [20] Husain, Z.,. “Fuzzy logic expert system for incipient fault diagnosis of power transformers”. , International Journal on Electrical Engineering and Informatics , 10: 300–317, (2018)
  • [21] Ranga, C., Chandel, A. K. & Chandel, R.,. “Expert system for condition monitoring of power transformer using fuzzy logic”. , Journal of Renewable and Sustainable Energy , 9: (2017)
  • [22] Ranga, C., Chandel, A. K. & Chandel, R.,. “Fuzzy logic expert system for optimum maintenance of power transformers”. , International Journal on Electrical Engineering and Informatics , 8: 835–849, (2016)
  • [23] Khlebtsov, A. P., Shilin, A. N., Rybakov, A. V. & Klyucharev, A. Y.,. “Development of a fuzzy expert system for power transformer diagnostics”. , Journal of Physics: Conference Series , 2091: (2021)
  • [24] Velasquez, R. M. A. & Lara, J. V. M.,. “Expert system for power transformer diagnosis”. , Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 , (2017) doi:10.1109/INTERCON.2017.8079640
  • [25] Qiu, S. et al.,. “Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review”. , Sensors , 23: (2023)
  • [26] Zhang, C. et al.,. “Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning”. , Future Generation Computer Systems , 108: 533–545, (2020)
  • [27] Hong, K., Jin, M. & Huang, H.,. “Transformer winding fault diagnosis using vibration image and deep learning”. , IEEE Transactions on Power Delivery , 36: 676–685, (2021)
  • [28] Elsisi, M. et al.,. “Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties”. , Measurement: Journal of the International Measurement Confederation , 190: (2022)
  • [29] Jin, Y., Hou, L. & Chen, Y.,. “A Time Series Transformer based method for the rotating machinery fault diagnosis”. , Neurocomputing , 494: 379–395, (2022)
  • [30] Pawar, R. R. & Deosarkar, S. B.,. “Health condition monitoring system for distribution transformer using Internet of Things (IoT)”. , Proceedings of the International Conference on Computing Methodologies and Communication, ICCMC 2017 , 2018–Janua: 117–122, (2017)
  • [31] Cheng, X. H. & Wang, Y.,. “The remote monitoring system of transformer fault based on the internet of things”. , Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011 , 1: 84–87, (2011)
  • [32] Al Mhdawi, A. K. & Al-Raweshidy, H. S.,. “A Smart Optimization of Fault Diagnosis in Electrical Grid Using Distributed Software-Defined IoT System”. , IEEE Systems Journal , 14: 2780–2790, (2020)
  • [33] Chi, Y., Dong, Y., Wang, Z. J., Yu, F. R. & Leung, V. C. M.,. “Knowledge-Based Fault Diagnosis in Industrial Internet of Things: A Survey”. , IEEE Internet of Things Journal , 9: 12886–12900, (2022)
  • [34] Mohamad, A. A. H., Mezaal, Y. S. & Abdulkareem, S. F.,. “Computerized power transformer monitoring based on internet of things”. , International Journal of Engineering and Technology(UAE) , 7: 2773–2778, (2018)
  • [35] Wu, Y., Sun, X., Zhang, Y., Zhong, X. & Cheng, L.,. “A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine”. , IEEE Access , 10: 17268–17286, (2022)
  • [36] Hoballah, A., Mansour, D. E. A. & Taha, I. B. M.,. “Hybrid Grey Wolf Optimizer for Transformer Fault Diagnosis Using Dissolved Gases Considering Uncertainty in Measurements”. , IEEE Access , 8: 139176–139187, (2020)
  • [37] Fan, J. et al.,. “Hybrid RVM-ANFIS algorithm for transformer fault diagnosis”. , IET Generation, Transmission and Distribution , 11: 3637–3643, (2017)
  • [38] Fang, J. et al.,. “A transformer fault diagnosis model using an optimal hybrid dissolved gas analysis features subset with improved social group optimization-support vector machine classifier”. , Energies , 11: (2018)
  • [39] Al-Janabi, S., Rawat, S., Patel, A. & Al-Shourbaji, I.,. “Design and evaluation of a hybrid system for detection and prediction of faults in electrical transformers”. , International Journal of Electrical Power and Energy Systems , 67: 324–335, (2015)
  • [40] Illias, H. A., Chai, X. R. & Abu Bakar, A. H.,. “Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis”. , Measurement: Journal of the International Measurement Confederation , 90: 94–102, (2016)
  • [41] El-kenawy, E. S. M. et al.,. “Feature Selection and Classification of Transformer Faults Based on Novel Meta-Heuristic Algorithm”. , Mathematics , 10: (2022)
  • [42] Wong, S. Y., Ye, X., Guo, F. & Goh, H. H.,. “Computational intelligence for preventive maintenance of power transformers”. , Applied Soft Computing , 114: 108129, (2022)
  • [43] Li, D., Wang, B. & Feng, J.,. “Simulation Analysis of Power Transformer Internal Short Circuit for Magnetic Flux Leakage Distribution Feature Extraction”. , Proceedings of 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023 , (2023) doi:10.1109/SAFEPROCESS58597.2023.10295835
  • [44] Chothani, N. & Patel, D.,. “Modern Method to Discriminate High Resistance Internal Winding Faults and Inrush Condition/External Faults in Power Transformer”. , 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2024 , (2024) doi:10.1109/ODICON62106.2024.10797598
  • [45] Abdusalam, O., Anayi, F. & Packianather, M.,. “Discrimination between internal current and external fault in three phase power transformer by Using alienation coefficient”. , 2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings , (2022) doi:10.1109/UPEC55022.2022.9917688
  • [46] Li, Y., Fang, J., Zhang, Y. & Xu, Y.,. “Analysis of Typical Cases of Transformer Internal Faults”. , 2022 2nd International Conference on Electrical Engineering and Control Science, IC2ECS 2022 , 1022–1025, (2022) doi:10.1109/IC2ECS57645.2022.10088112
  • [47] Low, S. H.,. “Three-phase Transformer Modeling”. , 2023 59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023 , (2023) doi:10.1109/Allerton58177.2023.10313400
  • [48] Pani, S. R., Bera, P. K. & Kumar, V.,. “Detection and classification of internal faults in power transformers using tree based classifiers”. , 9th IEEE International Conference on Power Electronics, Drives and Energy Systems, PEDES 2020 , (2020) doi:10.1109/PEDES49360.2020.9379641
  • [49] Bera, P. K., Isik, C. & Kumar, V.,. “Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators”. , IEEE Systems Journal , 15: 3450–3461, (2021)
  • [50] Xiu, Z. et al.,. “Analysis of an Internal Fault of Main Transformer”. , Lecture Notes in Electrical Engineering , 1054 LNEE: 767–776, (2023)
  • [51] Ahmadi, H., Vahidi, B. & Foroughi Nematollahi, A.,. “A simple method to detect internal and external short-circuit faults, classify and locate different internal faults in transformers”. , Electrical Engineering , 103: 825–836, (2021)
  • [52] Abu-Siada, A.,. “Improved consistent interpretation approach of fault type within power transformers using dissolved gas analysis and gene expression programming”. , Energies , 12: (2019)
  • [53] Abd El-Aal, R. A., Helal, K., Hassan, A. M. M. & Dessouky, S. S.,. “Prediction of Transformers Conditions and Lifetime Using Furan Compounds Analysis”. , IEEE Access , 7: 102264–102273, (2019)
  • [54] Wu, Y., Sun, X., Yang, P. & Wang, Z.,. “Transformer fault diagnosis based on Improved Particle Swarm Optimization to support Vector Machine”. , Journal of Physics: Conference Series , 1750: (2021)
  • [55] Al-Yozbaky, O. S. & Othman, R. A.,. “The Influence of Non-Sinusoidal Power Supply on Single-Phase Transformer Performance”. , Journal Européen des Systèmes Automatisés , 57: 1015, (2024)
  • [56] Landaburu, J.,. “INVESTIGATING SOME IMPORTANT PARAMETERS OF THE PDC MEASUREMENT TECHNIQUE FOR THE INSULATION CONDITION ASESSMENT OF POWER TRANSFORMER”. 1–23, (2016)
  • [57] Bai, P. & Jia, Y.,. “Simulation analysis of internal and external faults in transformer area”. , MATEC Web of Conferences , 382: 01041, (2023)
  • [58] Khalyasmaa, A. I., Senyuk, M. D. & Eroshenko, S. A.,. “High-Voltage Circuit Breakers Techn[1] S. Y. Wong, X. Ye, F. Guo, and H. H. Goh, ‘Computational intelligence for preventive maintenance of power transformers,’ Appl. Soft Comput., vol. 114, p. 108129, 2022, doi: 10.1016/j.asoc.2021.108129.i[1] S. Y. Wong,”. , IEEE Transactions on Power Delivery , 34: 1747–1756, (2019)
  • [59] Zhao, Z. et al.,. “Diagnosing Transformer Winding Deformation Faults Based on the Analysis of Binary Image Obtained from FRA Signature”. , IEEE Access , 7: 40463–40474, (2019)
  • [60] Kari, T., Gao, W., Tuluhong, A., Yaermaimaiti, Y. & Zhang, Z.,. “Mixed kernel function support vector regression with genetic algorithm for forecasting dissolved gas content in power transformers”. , Energies , 11: (2018)
  • [61] Lee, J. S., Seo, W. S. & Kang, S. H.,. “An Internal Fault Model of a Transformer Bank Using the UDM of Matlab/Simulink”. , 2023 International Conference on Advanced Power System Automation and Protection, APAP 2023 , 50–55, (2023) doi:10.1109/APAP59666.2023.10348495
  • [62] Ramesh, J., Shahriar, S., Al-Ali, A. R., Osman, A. & Shaaban, M. F.,. “Machine Learning Approach for Smart Distribution Transformers Load Monitoring and Management System”. , Energies , 15: 1–19, (2022) [63] ÖZÜPAK, Y.,. “Performing Structural Design and Modeling of Transformers Using ANSYS-Maxwell”. , Brilliant Engineering , 2: 38–42, (2021)
  • [64] Bacha, K., Souahlia, S. & Gossa, M.,. “Power transformer fault diagnosis based on dissolved gas analysis by support vector machine”. , Electric Power Systems Research , 83: 73–79, (2012) [65] Tenbohlen, S., Jagers, J. & Vahidi, F.,. “Standardized Survey of Transformer Reliability”. , Conference Proceedings of ISEIM , 2008–2011, (2017)
  • [66] Bai, C. F., Gao, W. S. & Liu, T.,. “Analyzing the impact of ambient temperature indicators on transformer life in different regions of Chinese mainland”. , The Scientific World Journal , 2013: (2013)
  • [67] Eslami, A.,. “A three-phase comprehensive methodology to analyze short circuits, open circuits and internal faults of transformers based on the comp ensation theorem”. , International Journal of Electrical Power and Energy Systems , 96: 238–252, (2018)
  • [68] Thangavelan, M., Prabavathi, K. & Ramesh, L.,. “Review on power transformer internal fault diagnosis”. , Journal of Electrical Engineering , 14: 1–7, (2014)
  • [69] Asadi, N. & Kelk, H. M.,. “Modeling, Analysis, and Detection of Internal Winding Faults in Power Transformers”. , IEEE Transactions on Power Delivery , 30: 2419–2426, (2015)
  • [70] Etumi, A. A. A. & Anayi, F. J.,. “The application of correlation technique in detecting internal and external faults in three-phase transformer and saturation of current transformer”. , IEEE Transactions on Power Delivery , 31: 2131–2139, (2016)
  • [71] Tenbohlen, S. et al.,. “Diagnostic measurements for power transformers”. , Energies , 9: (2016)
  • [72] Alkahdely, S. M. A. & Alsammak, A. N. B.,. “Normal operation and reverse action of on-load tap changing transformer with its effect on voltage stability”. , Bulletin of Electrical Engineering and Informatics , 12: 650–658, (2023)
  • [73] Alkahdely, S. M. A. & Alsammak, A. N. B.,. “Non-Limiting Operation of The On-Load Tap Changing Transformer, and Its Effect on Voltage Stability, with Regards to The Nineveh Electrical Grid”. , Przeglad Elektrotechniczny , 99: 181–186, (2023)
  • [74] Koch, M. & Prevost, T.,. “Analysis of dielectric response measurements for condition assessment of oil-paper transformer insulation”. , IEEE Transactions on Dielectrics and Electrical Insulation , 19: 1908–1915, (2012)
  • [75] Zuo, W., Yuan, H., Shang, Y., Liu, Y. & Chen, T.,. “Calculation of a health index of oil-paper transformers insulation with binary logistic regression”. , Mathematical Problems in Engineering , 2016: (2016)
  • [76] Martin, D., Cui, Y., Ekanayake, C., Hui, M. & Saha, T.,. “An updated model to determine the life remaining of transformer insulation”. , IEEE Transactions on Power Delivery , 30: 395–402, (2015)
  • [77] Norazhar, A. B., Abu-Siada, A. & Islam, S.,. “A review on chemical diagnosis techniques for transformer paper insulation degradation”. , 2013 Australasian Universities Power Engineering Conference, AUPEC 2013 , (2013) doi:10.1109/aupec.2013.6725476
  • [78] Alqudsi, A. Y. & Elhag, A. H.,. “A cost effective artificial intelligence based transformer insulation health index”. , 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems, CATCON 2017 - Proceedings , 2018–Janua: 108–111, (2017)
  • [79] Alyozbaky, O. S., Ab Kadir, M. Z. A. A., Izadi, M., Gomes, C. & Azis, N. B.,. “The behaviour of three phase three-leg 11KV transformer core type design under sinusoidal and non-sinusoidal operating conditions for different core materials”. , International Journal of Simulation: Systems, Science and Technology , 17: 27.1-27.8, (2017)
  • [80] Wouters, P. A. A. F., Van Schijndel, A. & Wetzer, J. M.,. “Remaining lifetime modeling of power transformers: Individual assets and fleets”. , IEEE Electrical Insulation Magazine , 27: 45–51, (2011)
  • [81] Alyozbaky, O. S.,. “The losses and temperature comparison in three-phase distribution transformer with various assembly core designs”. , Australian Journal of Electrical and Electronics Engineering , 15: 61–70, (2018)
  • [82] Oliveira, L. S. M. R. & Cardoso, A. J. M.,. “A permeance-based transformer model and its application to winding interturn arcing fault studies”. , IEEE Transactions on Power Delivery , 25: 1589–1598, (2010)
  • [83] Sun, C., Ohodnicki, P. R. & Stewart, E. M.,. “Chemical Sensing Strategies for Real-Time Monitoring of Transformer Oil: A Review”. , IEEE Sensors Journal , 17: 5786–5806, (2017)
  • [84] De Faria, H., Costa, J. G. S. & Olivas, J. L. M.,. “A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis”. , Renewable and Sustainable Energy Reviews , 46: 201–209, (2015)
  • [85] Cheng, L. & Yu, T.,. “Dissolved gas analysis principle-based intelligent approaches to fault diagnosis and decision making for large oil-immersed power transformers: A survey”. , Energies , 11: 1–69, (2018)
  • [86] Khalili Senobari, R., Sadeh, J. & Borsi, H.,. “Frequency response analysis (FRA) of transformers as a tool for fault detection and location: A review”. , Electric Power Systems Research , 155: 172–183, (2018)
  • [87] Gomez-Luna, E., Aponte Mayor, G., Gonzalez-Garcia, C. & Pleite Guerra, J.,. “Current status and future trends in frequency-response analysis with a transformer in service”. , IEEE Transactions on Power Delivery , 28: 1024–1031, (2013)
  • [88] Yao, C. et al.,. “Transformer winding deformation diagnostic system using online high frequency signal injection by capacitive coupling”. , IEEE Transactions on Dielectrics and Electrical Aljammal,
There are 83 citations in total.

Details

Primary Language English
Subjects Manufacturing and Industrial Engineering (Other)
Journal Section Review Article
Authors

Mustafa Aljammal

Omar Al-yozbaky

Submission Date February 2, 2025
Acceptance Date April 15, 2025
Publication Date July 25, 2025
Published in Issue Year 2025 Volume: 4 Issue: 2

Cite

APA Aljammal, M., & Al-yozbaky, O. (2025). A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis. Journal of Optimization and Decision Making, 4(2), 449-497.
AMA Aljammal M, Al-yozbaky O. A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis. Journal of Optimization and Decision Making. July 2025;4(2):449-497.
Chicago Aljammal, Mustafa, and Omar Al-yozbaky. “A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis”. Journal of Optimization and Decision Making 4, no. 2 (July 2025): 449-97.
EndNote Aljammal M, Al-yozbaky O (July 1, 2025) A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis. Journal of Optimization and Decision Making 4 2 449–497.
IEEE M. Aljammal and O. Al-yozbaky, “A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis”, Journal of Optimization and Decision Making, vol. 4, no. 2, pp. 449–497, 2025.
ISNAD Aljammal, Mustafa - Al-yozbaky, Omar. “A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis”. Journal of Optimization and Decision Making 4/2 (July2025), 449-497.
JAMA Aljammal M, Al-yozbaky O. A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis. Journal of Optimization and Decision Making. 2025;4:449–497.
MLA Aljammal, Mustafa and Omar Al-yozbaky. “A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis”. Journal of Optimization and Decision Making, vol. 4, no. 2, 2025, pp. 449-97.
Vancouver Aljammal M, Al-yozbaky O. A State-of-the-Art Review on Intelligent Models for Transformer Fault Monitoring and Diagnosis. Journal of Optimization and Decision Making. 2025;4(2):449-97.