Mathematical models of power transformers winding faults diagnosis based on voltage-current characteristics

  • Authors

    • Guy-de-patience Ftatsi Mbetmi
    • Fabrice Tsegaing Tchatchueng
    • Boussaibo Andre University Institute of Technology of Ngaoundere/Cameroon
    • Sametah Macine Ngong National School of Agro-Industrial Sciences
    • Ndjiya Ngasop Stephane National School of Agro-Industrial Sciences (ENSAI),
    2024-05-27
    https://doi.org/10.14419/9qpy4k87
  • Distribution Power Transformer; Diagnosis; Voltage–Current; Indicators Semicolon.

  • Power transformers are important components of electrical systems. Their failures are very costly, mainly because of the unavailability of electrical service they cause. Rapid and accurate diagnosis of internal transformer faults is a key factor of efficient and safe operation. Such diagnosis is usually carried out by experts. Several methods of power transformer windings diagnosis exist. The online diagnosis, the interpretation of results and the identification of various internal winding faults are criteria to be taken into account when choosing a method. The voltage-current method is a powerful method that has been successfully used as a diagnostic technique to detect power transformer winding faults. In this work we propose a mathematical models of power transformers winding faults based on voltage-current method. To achieve our goal, we modelled the transformer winding as a distributed network of similar R-L-C circuits. Then we used Matlab/Simulink to simulate the different winding faults on a number of discs ranging from 5 to 120. We defined four failures indicators and we studied their evolution for each fault. At the end we give for each fault the corresponding mathematical model. The transformer used in this work is a 15 MVA 22/0.415 kV distribution power transformer.

  • References

    1. Aslam, M., Haq, I.U., Rehan, M.S., Ali, F., Basit, A., Khan, M.I., Arbab, M.N., 2021. Health Analysis of Transformer Winding Insula-tion Through Thermal Monitoring and Fast Fourier Transform (FFT) Power Spectrum. IEEE Access 9, 114207–114217. https://doi.org/10.1109/ACCESS.2021.3104033.
    2. 16- Wang, M., 2003. Winding movement and condition monitoring of power transformers in service. University of British Columbia.
    3. Behjat, V., Vahedi, A., Setayeshmehr, A., Borsi, H., Gockenbach, E., 2011. Diagnosing Shorted Turns on the Windings of Power Transformers Based Upon Online FRA Using Capacitive and Inductive Couplings. IEEE Transactions on Power Delivery - IEEE TRANS POWER DELIVERY 26, 2123–2133. https://doi.org/10.1109/TPWRD.2011.2151285.
    4. Liu, Y., Ji, S., Yang, F., Cui, Y., Zhu, L., Rao, Z., Ke, C., Yang, X., 2015. A study of the sweep frequency impedance method and its application in the detection of internal winding short circuit faults in power transformers. IEEE Trans. Dielect. Electr. Insul. 22, 2046–2056. https://doi.org/10.1109/TDEI.2015.004977.
    5. Tahir, M., Tenbohlen, S., 2021. Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Fre-quency Response Measurements. Energies 14, 3227. https://doi.org/10.3390/en14113227.
    6. Babiy, M., Gokaraju, R., Garcia, J.C., 2011. Turn-to-turn fault detection in transformers using negative sequence currents, in 2011 IEEE Electrical Power and Energy Conference. Presented at the Energy Conference (EPEC), IEEE, Winnipeg, MB, Canada, pp. 158–163. https://doi.org/10.1109/EPEC.2011.6070187.
    7. Abu-Siada, A., Islam, S., 2012. A Novel Online Technique to Detect Power Transformer Winding Faults. IEEE Trans. Power Delivery 27, 849–857. https://doi.org/10.1109/TPWRD.2011.2180932.
    8. Zhao, X., Yao, C., Zhou, Z., Li, C., Wang, X., Zhu, T., Abu-Siada, A., 2020. Experimental Evaluation of Transformer Internal Fault Detection Based on V–I Characteristics. IEEE Transactions on Industrial Electronics 67, 4108–4119. https://doi.org/10.1109/TIE.2019.2917368.
    9. Chong, J., Abu-Siada, A., 2011. A novel algorithm to detect internal transformer faults, in: 2011 IEEE Power and Energy Society Gen-eral Meeting. Presented at the 2011 IEEE Power & Energy Society General Meeting, IEEE, San Diego, CA, pp. 1–5. https://doi.org/10.1109/PES.2011.6039472.
    10. Hurezeanu, I., Nicola, C., Sacerdotianu, D., Nicola, M., Aciu, A.-M., Nitu, M.C., 2016. Temperature control and monitoring system for power transformer windings using fiber optic sensors. https://doi.org/10.1109/ISFEE.2016.7803151.
    11. Haghjoo, F., Mostafaei, M., 2016. Flux-based method to diagnose and identify the location of turn-to-turn faults in transformers. IET Generation, Transmission & Distribution 10, 1083–1091. https://doi.org/10.1049/iet-gtd.2015.1180.
    12. Mostafaei, M., Haghjoo, F., 2016. Flux-based turn-to-turn fault protection for power transformers. IET Generation, Transmission & Distribution 10, 1154–1163. https://doi.org/10.1049/iet-gtd.2015.0738.
    13. Small, B.J., Abu-Siada, A., 2011. A new method for analysing transformer condition using frequency response analysis, in: 2011 IEEE Power and Energy Society General Meeting. Presented at the 2011 IEEE Power & Energy Society General Meeting, IEEE, San Diego, CA, pp. 1–5. https://doi.org/10.1109/PES.2011.6039470.
    14. Abu-Siada, A., Islam, S., 2012. A Novel Online Technique to Detect Power Transformer Winding Faults. IEEE Trans. Power Delivery 27, 849–857. https://doi.org/10.1109/TPWRD.2011.2180932.
    15. Dick, E.P., Erven, C.C., 1978. Transformer Diagnostic Testing by Frequuency Response Analysis. IEEE Trans. on Power Apparatus and Syst. PAS-97, 2144–2153. https://doi.org/10.1109/TPAS.1978.354718.
    16. Zhao, C., Ruan, J., Du, Z., Liu, S., Yu, Y., Zhang, Y., 2008. Calculation of parameters in transformer winding based on the model of multi-conductor transmission line, in 2008 International Conference on Electrical Machines and Systems. Presented at the 2008 Inter-national Conference on Electrical Machines and Systems, pp. 463–467.
    17. Ragavan, K., Satish, L., 2005. An efficient method to compute transfer function of a transformer from its equivalent circuit. IEEE Transactions on Power Delivery 20, 780–788. https://doi.org/10.1109/TPWRD.2004.834345.
    18. Ibrahim, K.H., Korany, N.R., Saleh, S.M., 2022. Effects of power transformer high-frequency equivalent circuit parameters non-uniformity on fault diagnosis using SFRA test. Ain Shams Engineering Journal 13, 101674. https://doi.org/10.1016/j.asej.2021.101674.
    19. Abbasi, A.R., 2022. Fault detection and diagnosis in power transformers: a comprehensive review and classification of publications and methods. Electric Power Systems Research 209, 107990. https://doi.org/10.1016/j.epsr.2022.107990.
    20. Yazdani Asrami, M., Samadaei, E., Darvishi, S., Taghipour, M., 2010. Modeling and simulation of transformer winding fault using MATLAB/SIMULINK. International Review on Modelling and Simulations 3, 1103–1109.

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  • How to Cite

    Ftatsi Mbetmi, G.- de- patience, Tsegaing Tchatchueng, F., Andre, B. . . ., Macine Ngong , S. ., & Ngasop Stephane , N. . (2024). Mathematical models of power transformers winding faults diagnosis based on voltage-current characteristics. International Journal of Engineering & Technology, 13(1), 156-166. https://doi.org/10.14419/9qpy4k87