Fault Diagnosis of a Transformer using Fuzzy Model and Unsupervised Learning

  • Akshita Dhiman
  • Rajesh Kumar


In this paper a power transformer fault diagnosis system (PDFDS) based on soft computing and computational intelligence is proposed. Fault diagnosis and analysis is an integral part of operational reliability. Systems like SCADA collect data of various equipment in power system network, however, fails to provide a critique fault diagnosis for the same which further leads to additional cost of replacing the equipment. This paper proposes a supervised-unsupervised predictive model for the data collected from various power transformers across Himachal Pradesh and IEC 10 database. To identify the different fault types in a transformer a fuzzy model is developed using the DGA interpretation techniques. Since, not all data samples in the collected dataset fall under the standards specified in the ratio tables it thus becomes difficult to identify the type of fault for such cases. To overcome this an improved fuzzy model with unsupervised clustering algorithm or Fuzzy Clustering means is used. Employing this improved model optimizes the data before feeding it to the different predictive machine learning models. Further, a particle swarm optimization algorithm with passive congregation is employed to optimize the performance of these machine learning models.

Keywords: Dissolved Gas Analysis; Fuzzy Model; Fuzzy- C means Clustering; Multiclass Classification; Support Vector Machine, Particle Swarm Optimization


Download data is not yet available.


[1] Nitchamon Poonnoy, Cattareeya Suwanasri, and Thanapong Suwanasri “Fuzzy Logic Approach to Dissolved Gas Analysis for Power Transformer Failure Index and Fault Identification”, Energies, 14(1), 36 2021.
[2] Vladimir Mikhailovich Levin, Ammar Abdulazeez Yahya, Diana A. Boyarova, “Predicting the technical condition of the power transformer using fuzzy logic and dissolved gas analysis method”, International Journal of Electrical and Computer Engineering 12(2):1139, April 2022.
[3] Omar M. Elmabrouk, Farag A. Masoud, Naji S. Abdelwanis, “Diagnosis of Power Transformer Faults using Fuzzy Logic Techniques Based on IEC Ratio Method”, The 6th International Conference on Engineering & MIS 2020.
[4] Khmais Bacha Seifeddine Souahlia Moncef Gossa, “Power transformer Fault Diagnosis based on dissolved gas analysis by support vector machine”, Electric Power Systems Research, Vol 83, Issue 1, 2012.
[5] U. Mohan Rao; I. Fofana; K. N. V. P. S. Rajesh; P. Picher, “Identification and Application of Machine Learning Algorithms for Transformer Dissolved Gas Analysis“ , IEEE Transactions on Dielectrics and Electrical Insulation, Vol: 28, Issue: 5, October 2021.
[6] Zhenghong Peng, Bin Song, “ Research on Transformer Fault Diagnosis Expert System Based on DGA Database”, Second International Conference on Information and Computing Science, 2009.
[7] Ricardo D.Medina Diego, A.Zaldivar, Andrés A.Romero, Jefferson Zuñiga, Enrique E.Mombello, “ A fuzzy inference-based approach for estimating power transformers risk index”, Electric Power Systems Research, Volume 209, 2022.
[8] Hasmat Malik, Rajneesh Sharma, Sukumar Mishra , “ Fuzzy reinforcement learning based intelligent classifier for power transformer faults”, ISA Transactions, Volume 101, 2020.
[9] Ekojono, Rahman Azis Prasojo, Meyti Eka Apriyani & Anugrah Nur Rahmanto, “Investigation on machine learning algorithms to support transformer dissolved gas analysis fault identification.
[10] Syed Mofizul Islam, Tony Wu, Gerald Ledwich, “ A Novel Fuzzy Approach to Tranformer Fault Diagnosis”, IEEE Transactions on Dielectrics and electrical Insulation, Vol. 7 No.2 April 2000.
[11] Xiaohui Li, Huaren Wu, Danning W, “ DGA Interpretation Schemen Derived from Case Study ”, IEEE Transaction on Power Delivery, Vol.26, No.2 April 2011.
[12] Michel Duval, “Review of Faults Detectable by Gas-in-Oil Analysis in Transformers, IEEE Electrical Insulation Magazine, May/June-Vol 18, No.3, 2002.
[13] Michel Duval, Alfonso dePablo, “Interpretation of Gas-in-Oil Analysis using New IEC Publication 60599 and IEC 10 Databases”, IEEE Electrical Insulation magazine, March/April, Vol 17, No.2 , 2001.
[14] ,L.V.Ganyun, Cheng Haozhong, Zhai Haibao Dong Lixin, “ Fault diagnosis of power transformer based on multi-layer SVM classifier”, Electric Power Systems Research, Volume 74, Issue 1, 2005 Springer Link, March 2022
[15] Ali Abdo , Hongshun Liu * , Hongru Zhang , Jian Guo , Qingquan Li, “ A New Model of Faults Classification in Power Transformers based on Data Optimization Method”, Electric Power Systems Research 200 (2021)
[16] Abraão G. C. Menezes, Mateus M. Araujo , Otacilio M. Almeida, Fabio R. Barbosa, and Arthur P. S. Braga., “Induction of Decision Trees to Diagnose Incipient Faults in Power Transformers”, IEEE Transactions On Dielectrics And Electrical Insulation, Vol. 29, No. 1, February 2022
[17] Qiang Liu; Guoqiang Huang; Chen Mao; Yu Shang; Fan Wang, “ Recognition of Dissolved Gas in Transformer Oil by Ant Colony Optimization Support Vector Machine”, IEEE International Conference on High Voltage Engineering and Application (ICHVE), 2016
0 Views | 0 Downloads
How to Cite
Dhiman, A., & Kumar, R. (2023). Fault Diagnosis of a Transformer using Fuzzy Model and Unsupervised Learning. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 9(1), 55-60. https://doi.org/10.33130/AJCT.2023v09i01.011