Review on Transformer Fault Detection and Diagnosis Algorithms


  • Mohammed Misbahul Islam
  • Mrs. Madhu Upadhyay



Power Transformer, DGA, Diagnostics Methods, Monitoring And Transformer.


The method of fault diagnosis based on dissolved gas analysis (DGA) is of great importance to detect possible failures in the transformer and to improve the safety of the electrical system. The DGA data of the transformer in the smart grid has the characteristics of a large amount, different types and a low density of values. Since the power transformer is an important type of power supply in the electrical network, this document provides a complete overview of the power transformer and describes how to diagnose faults. Furthermore, on-line monitoring, the method of fault diagnosis and condition-based maintenance strategy decision-making method as also have been described. The paper presents detailed literature on the recent advancements and methods being adopted by various authors on fault detection.


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Author Biographies

Mohammed Misbahul Islam

M. Tech Scholar

NRI Institute of Research & Technology

Bhopal, Madhya Pradesh, India

Mrs. Madhu Upadhyay

Head of Department

NRI Institute of Research &Technology

Bhopal, Madhya Pradesh, India


M.R. Barzegaran and M. Mirzaie, “Detecting the position of winding short circuit faultsin transformer using high frequency analysis,” European Journal of Scientific Research, Vol. 23, 2008, pp. 644-658.

N.C. Joshi, Y.R. Sood “Transformer Internal Winding Faults Diagnosis Methods: A Review” MIT International Journal of Electrical and Instrumentation Engineering, Vol. 2, No. 2, Aug. 2012, pp. (77-81).

Xue Wang, Tao Han “Transformer Fault Diagnosis Based on Stacking Ensemble Learning”, 02 October 2020.

Ibrahim B. M. Taha, Sherif S. M. Ghoneim “A Fuzzy Diagnostic System for Incipient Transformer Faults Based on DGA of the Insulating Transformer Oils” IREE, Vol 11, No 3 (2016).

Huo-Ching Sun, Yann-Chang Huang “Fault Diagnosis of Power Transformers Using Computational Intelligence: A Review” 2012 Energy Procedia 14:1226-1231.

China preventive test code for electric power equipment (DL/T597-1996), Beijing : Electric power press,1997.

SUN Hui, LI Wei-dong, SUN Qi-zhong, “Electric power transformer fault diagnosis using decision tree ”, Proceedings of the CSEE , vol. 21(2),pp.50-55,2001.

China Power Equipment Co.500KV transformer quality compilation, Beijing: Ministry of electric power research institute, 1996.

DengHua Mei, HuaQing Min, “A Fuzzy Information Optimization Processing Technique for Monitoring the Transformer in Neural-Network On-line ”, Proceedings of 2005 IEEE International Conference on Dielectric Liquids, Cambria, Portugal,pp.273-275 ,2005.

Lin Jin-po, Zhao Ji-yin, ZhengRui-rui, Liu Yu, “Fault Diagnosis System of Transformer Based on Gas Chromatography ”, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics , Dalian China , pp.809-813, 2006.

Lzzularab M A, Aly G E M., Mansour D A, “On-line Diagnosis of Incipient Faults and Cellulose Degradation Based on Artificial Intelligence Methods ” 2004 International Conference on Solid Dielectric , Toulouse, France,pp.5-9,2004.

ZHANG Chuan, WANG Fu, “Application of Photo- Acoustic Spectroscopy Technology to Dissolved Gas Analysis in Oil of Oi-l Immersed Power Transformer”, High Voltage Engineering, vol. 31(2),pp.84-86,2005.

ZHANG Hao-yang, CAI Zhi-yuan, ZHANG Jun-yang, “An Overview of On-line Monitoring Technology for Dissolved Gas Content in Transformer Oil”, Northeast Electric Power Technology, vol.8,pp.48-50,2006.

YANG Qiping, XueWude, Lanzhida, “Application of Group Sensor for Transformer On-line Monitoring ”, 2005 IEEE /PES Transmission and Distribution Conference & Exhibition, Dalian China, pp.1-4,2005.

GUO Yu, “Online Measurement of Transformer Winding DC Resistance”, North China Electric Power University, March 2012.

WANG Fu-zhongWANGHai-ling, “220 kV transformer condition assessment strategy based on fuzzy theory”, Journal of henan polytechnic university(natural science), vol.33(3),pp.349-353,2014.

Guardado J L, Naredo J L, Moreno P, “A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis ” , IEEE Trans Power Del,vol.16(4),pp. 643?647,2001.

AN Shu-li, SONG Kun, LIU Fang, “A Case Study on Fault Diagnosis of 500 kV Transformer with Gas Chromatography Method”, High voltage apparatus,vol. 44(4),pp.381-382,2008.

YANG Ting-fang, “Study on New Techniques of Online Monitoring and Fault Diagnosis for Power Transformer”, Wuhan: Huazhong University of Science and TechnologyApril 2008.

IEEE guide for the interpretation of gases generated in oil-immersed transformers. IEEE Standard C57.104-2008.

Guide for the sampling of gases and of oil-filled electrical equipment and for the analysis of free and dissolved gases. IEC Standard 60567, 2005.

Duval M. A review of fault detectable by gas-in-oil analysis in transformers. IEEE Electrical Insulation Magazine 2002, 18:8–17.

Duval M, Dukarm J. “Improving the reliability of transformer gas-in-oil diagnosis,” IEEE Electrical Insulation Magazine 2005,21:21–27.

Michel Duval, Laurent Lamarre “The Duval Pentagon-A New Complementary Tool for the Interpretation of Dissolved Gas Analysis in Transformers” December 2014IEEE Electrical Insulation Magazine 30(6):9-12




How to Cite

Islam, M. M., & Upadhyay, M. M. . (2021). Review on Transformer Fault Detection and Diagnosis Algorithms. SMART MOVES JOURNAL IJOSCIENCE, 7(12), 27–33.