Mechanical Fault Types Detection in Transformer Windings Using ‎Interpretation of Frequency Responses via Multilayer Perceptron

Document Type : Research paper


1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

2 Faculty of Electrical and Computer engineering, University of Tabriz, Tabriz, Iran

3 High-Voltage Research Group, Niroo Research Institute, Tehran, Iran

4 Iran Grid Secure Operation Research Center, Amirkabir University of Technology, Tehran, Iran

5 Institute of Power Transmission and High Voltage Technology, University of Stuttgart, Stuttgart, Germany


The Frequency Response Analysis (FRA) technique has advantages in identifying faults related to power transformers, but it suffers from the interpretation of frequency responses. This paper presents an approach based on statistical indices and Artificial Neural Network (ANN) methods to interpret frequency responses. The proposed procedure divides frequency responses into four frequency regions based on frequency resonances and anti-resonances. Then, Lin’s Concordance Coefficient (LCC) index is used as one of the most appropriate numerical indices to extract features of the four frequency regions. Finally, the Multilayer Perceptron (MLP) neural network is trained by the extracted features to identify and differentiate the types of winding faults. Besides, other intelligent algorithms such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN), and Radial Basis Function (RBF) neural network have been employed to compare the classification results. The proposed techniques have been practically implemented. The Axial Displacement (AD) and Disk Space Variation (DSV) faults are applied as two common mechanical faults in different locations and intensities on the 20kV windings of a 1.6MVA distribution power transformer and their corresponding frequency responses are calculated. Frequency responses calculated from the AD and DSV faults constitute the MLP input data set. The network is trained with part of the input data, and the rest of the data is allocated to validate and test the network. The results show that the suggested method has more proper performance than others using the phase component of the frequency responses in interpreting frequency responses and separation and identifying various mechanical fault types of transformer windings.


  1. Arguence and F. Cadoux, “Sizing power transformers in power systems planning using thermal rating”, Int. J. Electr. Power Energy Syst., vol. 118, pp. 105781, 2020.
  2. Moradzadeh et al., “Locating inter-turn faults in transformer windings using isometric feature mapping of frequency response traces”, IEEE Trans. Ind. Inf., 2020.
  3. Ardeshiri et al., “Introduction and literature review of power system challenges and issues” in Application of machine learning and deep learning methods to power system problems, Springer, 2021.
  4. Babaei and M. Moradi “Novel method for discrimination of transformers faults from magnetizing inrush currents using wavelet transform”, Iranian J. Sci. Tech. Trans. Electr. Eng., 2021.
  5. Behkam et al., “Generalized regression neural network application for fault type detection in distribution transformer windings considering statistical indices”, Int. J. Comput. Math. Electr. Electron. Eng., Vol. 41, pp. 381-409, 2022.
  6. Tarimoradi and G. B. Gharehpetian, “Novel calculation method of indices to improve classification of transformer winding fault type, location, and extent”, IEEE Trans. Ind. Inf., vol. 13, no. 4, pp. 1531-40, 2017.
  7. Moradzadeh and K. Pourhossein, “Short circuit location in transformer winding using deep learning of its frequency responses”, Proc. 2019 Int. Aegean Conf. Electr. Machines Power Electron.,, 2019.
  8. Akhmetov et al., “A new diagnostic technique for reliable decision-making on transformer FRA data in inter-turn short-circuit condition”, IEEE Trans. Ind. Inf., 2020.
  9. Moradzadeh and K. Pourhossein, “Location of disk space variations in transformer winding using convolutional neural networks”, 54th Int. Universities Power Eng. Conf., 2019.
  10. Rahimpour et al., “Transfer function method to diagnose axial displacement and radial deformation of transformer windings”, IEEE Trans. Power Del., vol. 18, no. 2, pp. 493-505, 2003.
  11. Pourhossein et al., “A probabilistic feature to determine type and extent of winding mechanical defects in power transformers”, Electr. Power Syst. Res., vol. 82, no. 1, pp. 1-10, 2012.
  12. Rahimpour, M. Jabbari, and S. Tenbohlen, “Mathematical comparison methods to assess transfer functions of transformers to detect different types of mechanical faults”, IEEE Trans. Power Del., vol. 25, no. 4, pp. 2544-55, 2010.
  13. Khalili Senobari, J. Sadeh, and H. Borsi, “Frequency response analysis (FRA) of transformers as a tool for fault detection and location: A review”, Electr. Power Syst. Res., vol. 155. pp. 172-83, 2018.
  14. H. Samimi and S. Tenbohlen, “FRA interpretation using numerical indices: State-of-the-art”, Int. J. Electr. Power Energy Syst., vol. 89, pp. 115-25, 2017.
  15. H. Samimi et al., “Evaluation of numerical indices for the assessment of transformer frequency response”, IET Gener. Trans. Distrib., vol. 11, no. 1, pp. 218-27, 2017.
  16. Tahir, S. Tenbohlen, and S. Miyazaki, “Analysis of statistical methods for assessment of power transformer frequency response measurements”, IEEE Trans. Power Del., 2020.
  17. Bigdeli, D. Azizian, and G. B. Gharehpetian, “Detection of probability of occurrence, type and severity of faults in transformer using frequency response analysis based numerical indices”, Measurement, vol. 168, pp. 108322, 2021.
  18. Maulik and L. Satish, “Localization and estimation of severity of a discrete and localized mechanical damage in transformer windings: Analytical approach”, IEEE Trans. Dielectrics Electr. Insulation, vol. 23, pp. 1266-74, 2016.
  19. Pham and E. Gockenbach, “Analysis of physical transformer circuits for frequency response interpretation and mechanical failure diagnosis”, IEEE Trans. Dielectrics Electr. Insulation, vol. 23, pp. 1491-9, 2016.
  20. Ragavan and L. Satish, “Localization of changes in a model winding based on terminal measurements: experimental study”, IEEE Trans. Power Del., vol. 22, no. 3, pp. 1557-65, 2007.
  21. Torkaman, V. Naeini, “Recognition and location of power transformer turn to turn fault by analysis of winding imposed forces”, J. Oper. Autom. Power Eng., vol. 7, pp. 227-34, 2019.
  22. Bagheri, Z. Moravej, and G. B. Gharehpetian, “Classification and discrimination among winding mechanical defects, internal and external electrical faults, and inrush current of transformer”, IEEE Trans. Ind. Informatics, vol. 14, no. 2, pp. 484-93, 2018.
  23. Zhao et al., “Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA signature”, IEEE Access, vol. 7, pp. 40463-74, 2019.
  24. J. Ghanizadeh and G. B. Gharehpetian, “ANN and cross-correlation based features for discrimination between electrical and mechanical defects and their localization in transformer winding”, IEEE Trans. Dielectrics Electr. Insulation, vol. 21, pp. 2374-82, 2014.
  25. Moradzadeh and K. Pourhossein, “Early detection of turn-to-turn faults in power transformer winding: an experimental study”, Proc. 2019 Int. Aegean Conf. Electr. Machines Power Electron., 2019.
  26. Zhao et al., “Interpretation of transformer winding deformation fault by the spectral clustering of FRA signature”, Int. J. Electr. Power Energy Syst., vol. 130, 2021.
  27. Pourhossein et al., “A vector-based approach to discriminate radial deformation and axial displacement of transformer winding and determine defect extent”, Electr. Power Components Syst., vol. 40, pp. 597-612, 2012.
  28. Mehran and S. Tenbohlen. “Transformer winding condition assessment using feedforward artificial neural network and frequency response measurements”, Energies, vol. 14, no. 11, 2021.
  29. Bigdeli and A. Abu-Siada, “Clustering of transformer condition using frequency response analysis based on k-means and GOA”, Electr. Power Syst. Res., vol. 202, 2022.
  30. Bigdeli, “Hybrid k-means-PSO technique for transformer insulation moisture determination in the production stage based on frequency response analysis”, Iran. J. Electr. Electron. Eng., vol. 18, 2022.
  31. Bigdeli, “Performance of mathematical indices in transformer condition monitoring using k-NN based frequency response analysis”, AUT J. Electr. Eng., vol. 53, no. 1, 2021.
  32. Haykin, Neural Networks and Learning Machines, vol. 3. 2008.
  33. Shahriyari and H. Khoshkhoo, “A deep learning-based approach for comprehensive rotor angle stability‎ assessment‎”, J. Oper. Autom. Power Eng., vol. 10, no. 2, 2022.
  34. Behjat, A. Shams, V. Tamjidi, “Characterization of power transformer electromagnetic forces affected by winding faults”, J. Oper. Autom. Power Eng., vol. 6, no. 1, 2018.
  35. Zakipour et al., “Efficiency improvement of the flyback converter based on high frequency transformer winding rearrangement”, J. Oper. Autom. Power Eng., vol. 8, no. 3, 2020.
  36. Secue, E. Mombello and C. Cardoso, “Review of sweep frequency response analysis -sfra for assessment winding displacements and deformation in power transformers”, IEEE Latin America Trans., vol. 5, no. 5, pp. 321-8, 2007.
  37. Moradzadeh and K. Pourhossein, “Early detection of turn-to-turn faults in power transformer winding: an experimental study”, Proc. 2019 Int. Aegean Conf. Electr. Machines Power Electron., 2019.
  38. Moradzadeh et al., “Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings”, Appl. Sci., vol. 10, no. 11, p. 3829, 2020.
  39. Bin Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks”, IEEE Int. Conf. Neural Net. Conf. Proc., 2004.
  40. Jakkula, “Tutorial on Support Vector Machine (SVM)”, Sch. EECS, Washingt. State Univ., 2011.
  41. Segal, M. Kothari, and S. Madnani, “Radial basis function (RBF) network adaptive power system stabilizer”, IEEE Trans. Power Syst., vol. 15, no. 2, pp. 722-7, 2000.
  42. Lin, T. Liang and S. Chen, “Estimation of battery state of health using probabilistic neural network”, IEEE Trans. Ind. Informatics, vol. 9, no. 2, pp. 679-85, 2013.
Volume 11, Issue 1
April 2023
Pages 11-21
  • Receive Date: 22 July 2021
  • Revise Date: 04 January 2022
  • Accept Date: 15 January 2022
  • First Publish Date: 26 January 2022