Performance of Mathematical Indices in Transformer Condition Monitoring Using k-NN Based Frequency Response Analysis

Document Type : Research Article


Department of Electrical Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran


Despite the development of the use of frequency response analysis (FRA) in condition monitoring of power transformers, how to interpret the results of FRA measurements has not yet been standardized. Therefore, proposing new methods to interpret the results of FRA measurements in research works. is followed by a great interest by researchers. This paper proposes a k-nearest neighbor (k-NN) based method for condition monitoring of the transformers, using the results of FRA measurements. First, the necessary measurements are performed on healthy and faulty transformers (under different fault conditions), and the required database is created. Later, by extracting the peak (resonance) and trough (anti-resonance) points of the measured transfer functions from the transformer, several mathematical features for training and validation of k-NN are extracted. Finally, by applying the data obtained from actual transformers, the performance of k-NN in different states is evaluated and compared. The results show that the proposed method is able to determine the condition of the transformer (whether it is healthy or defective) with high accuracy, and if it is defective, identify the type of defect. In addition, in order to prove the ability of k-NN, a comparison is made with the results of the artificial neural network (ANN).


Main Subjects

[1]    Karami, E., Koohsari, S., Mazhari, S., “Dynamic Harmonic Analysis of Long Term over Voltages Based on Time Varying Fourier series in Extended Harmonic Domain”, AUT Journal of Electrical Engineering, Vol. 48, No. 1, pp. 29-39, 2016.
[2]    Jiang, J., Zhou, L., Gao, Sh., Li, W., Wang, D., “Frequency Response Features of Axial Displacement Winding Faults in Autotransformers with Split Windings”, IEEE Transactions on Power Delivery, Vol. 33, No. 4, pp. 1699-1706, 2018.
[3]    Zhou, L., et al., “FRA modelling for diagnosing axial displacement of windings in traction transformers”, IET Electric Power Applications, Vol. 13, No. 12, pp. 2121-2127, 2019.
[4]    Zhou, L., Lin, T., Zhou, X., Gao, Sh., Wu, Zh., Zhang, Ch., “Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer”, IEEE Transactions on Transportation Electrification, Vol. 6, No. 2 , pp. 625-634, 2020.
[5]    Bigdeli, M., Vakilian, M., Rahimpour, E., “Transformer Winding Faults Classification Based on Transfer Function Analysis by Support Vector Machine”, IET Electric Power Applications, Vol. 6, No. 5, 2012.
[6]    Rahimpour, E., Christian, J., Feser, K., and Mohseni, H., “Transfer function method to diagnose axial displacement and radial deformation of transformer winding”, IEEE Transaction on Power Delivery, Vol. 18, No. 2, pp. 493–505, 2003.
[7]    IEEE Guide for the Application and Interpretation of Frequency Response Analysis for Oil-Immersed Transformers, IEEE Std C57.149, 2012.
[8]    IEC Standard on Power Transformers, Part 18: Measurement of Frequency Response, IEC 60076-18, 2012.
[9]    Zhao, X. Yao, Ch., Li, Ch., Zhang, Ch., Dong, Sh., Abu-Siada, A., Li, R., “Experimental evaluation of detecting power transformer internal faults using FRA polar plot and texture analysis”, International Journal of Electrical Power & Energy Systems, Vol. 108, pp. 1-8, 2019.
[10] Samimi, M. H., Hillenbrand, Ph., Tenbohlen, S., Shayegani Akmal, A. A., Mohseni, Faiz, J., “Investigating the applicability of the finite integration technique for studying the frequency response of the transformer winding”, International journal of Electrical Power and Energy Systems, Vol. 110, pp. 411–418, 2019.
[11] Jianqiang, Ni., Zhongyong, Zh., Shan, T., Yu, Ch., Chenguo, Y., Chao, T., “The actual measurement and analysis of transformer winding deformation fault degrees by FRA using mathematical indicators”, Electric Power Systems Research, Vol. 184, pp. 1-11, 2020.
[12] Mahvi, M., Behjat, V., Mohseni, H., “Analysis and interpretation of power auto-transformer winding axial displacement and radial deformation using frequency response analysis”, Engineering Failure Analysis, Vol. 113, 104549, 2020.
[13] Samimi, M. H., Tenbohlen, S., Shayegani Akmal, A. A., Mohseni, H., “Evaluation of numerical indices for the assessment of transformer frequency response”, IET Generation, Transmission & Distribution, Vol. 11, No. 1, pp. 218-227, 2017.
[14] Samimi, M. H., Tenbohlen, S., Shayegani Akmal, A. A., Mohseni, H., “Improving the numerical indices proposed for the FRA interpretation by including the phase response”, International journal of Electrical Power and Energy Systems, Vol. 83, pp. 585–593, 2016.
[15] Samimi, M. H., Tenbohlen, S., “FRA interpretation using numerical indices: State-of-the-art”, International Journal of Electrical Power and Energy Systems, Vol. 89, pp. 115–125, 2017.
[16] Bigdeli, M., Azizian, D., Gharehpetian, G. B., “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.
[17] Devadiga, A., et al., “Winding turn-to-turn short-circuit diagnosis using FRA method: sensitivity of measurement configuration”, IET Science, Measurement & Technology, Vol. 13, No. 1, pp. 17-24, 2019.
[18] Liu, J., Zhao, Zh., Tang, Ch., Yao, Ch., Li, Ch., and Islam, S., “Classifying Transformer Winding Deformation Fault Types and Degrees using FRA based on Support Vector Machine”, IEEE Access, Vol. 7, pp. 112494 – 112504, 2019.
[19] Bigdeli, M., Vakilian, M., Rahimpour, E., “A Probabilistic Neural Network Classifier Based Method for Transformer Winding Fault Identification through its Transfer Function Measurement”, International Transactions on Electrical Energy Systems, Vol. 23, No. 3, 2013.
[20] Benmahamed, Y., Teguar, M., Boubakeur, A., “Application of SVM and KNN to Duval Pentagon 1 for Transformer Oil Diagnosis”, IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 24, No. 6; pp. 3443-3451, 2017.
[21] Mo, W., Kari, T., Wang, H., Luan, L., Gao, W., “Fault Diagnosis of Power Transformer Using Feature Selection Techniques and KNN”, 3rd IEEE International Conference on Computer and Communications, 2017.
[22] Rahmatian, M., Vahidi, B., Ghanizadeh, A., J., Gharehpetian, G. B., Alehosseini, H. A., “Insulation failure detection in transformer winding using cross-correlation technique with ANN and k-NN regression method during impulse test”, International Journal of Electrical Power and Energy Systems, Vol. 53, pp. 209-218, 2013.
[23] Doshi, S., Shrimali, M., Rajendra, Sh., K., Sharma, M., “Tunable Q-Factor Wavelet Transform for Classifying Mechanical Deformations in Power Transformer”, 5th International Conference on Signal Processing and Integrated Networks (SPIN), 2018.
[24] Pleite, J., Gonzalez, C., Vazquez, J., Lazaro, A., “Power Transformer Core Fault Diagnosis Using Frequency Response Analysis,” IEEE MELECON, Benalmadena (Malaga), Spain, 2006.
[25] Abu-Siada, A., Mosaad, M. I., Kim, D. W., El-Naggar, M. F., “Estimating Power Transformer High frequency Model Parameters using Frequency Response Analysis”, IEEE Transactions on Power Delivery, Vol. 35, No. 3, pp. 1267-1277, 2020.
[26] Rahimpour, E., Jabbari, M., Tenbohlen, S., “Mathematical Comparison Methods to Assess Transfer Functions of Transformers to Detect Different Types of Mechanical Faults”, IEEE Transactions on Power Delivery, Vol. 25,
[27] Rahimpour, E., Gorzin, D., “A new method for comparing the transfer function of transformers in order to detect the location and amount of winding faults”, Electrical Engineering, Vol. 88, No. 5, pp. 411-416, 2006.
[28] Wimmer, R., Tenbohlen, S., Heindl, M., Kraetge, A., Krüger, M., Christian J., “Development of algorithms to assess the FRA”, In: Proc. of 15th international symposium on high voltage engineering, T7-523; 2007.
[30] Campos, G. O., et al., “On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study”, Data Mining and Knowledge Discovery, Vol. 30, pp. 891–927, 2016.
[31] Haykin, S., “Neural Networks and Learning Machines Third Edition”, Pearson, Prentice Hall publication, 2009.
[32] Rodriguez, J. D., Perez, A., Lozano, J. A., Sensitivity Analysis of K-Fold Cross Validation in Prediction Error Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 32, No. 3, pp. 569-575, 2010.