1. bookVolume 18 (2018): Issue 4 (August 2018)
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
access type Open Access

Frequency and time fault diagnosis methods of power transformers

Published Online: 14 Aug 2018
Volume & Issue: Volume 18 (2018) - Issue 4 (August 2018)
Page range: 162 - 167
Received: 17 Apr 2018
Accepted: 10 Jul 2018
Journal Details
License
Format
Journal
eISSN
1335-8871
First Published
07 Mar 2008
Publication timeframe
6 times per year
Languages
English
Abstract

The authors describe experimental and theoretical analyses of faults of power transformer winding. Faults were caused by mechanical effect of short-circuit currents. Measurements of transformer were carried out in high-voltage laboratory. Frequency and time diagnostic methods (method SFRA - Sweep Frequency Response Analysis, impact test) were used for the analyses. Coils of transformer windings were diagnosed by means of the SFRA method and the time impact test. The analyzed methods had a significant sensitivity to a relatively small deformation of coil. In the analysis a new technique for analyzing the effects of short-circuit currents is introduced. This technique is developed for high-voltage transformers (different types of power). The proposed analyses show that it is necessary to analyze the value of short-circuit current. Short-circuit current represents a danger for the operation of the power transformer. The proposed approach can be used for other types of transformers. Moreover, the presented techniques have a potential application for fault diagnosis of electrical equipment such as: transformers and electrical machines.

Keywords

[1] Hrabovcova, V., Rafajdus, P., Franko, M., Hudák, M. (2009). Measuring and Modelling of Electrical Machines. Žilina, Slovakia: EDIS. ISBN 978-80-8070- 924-2. (in Slovak)Search in Google Scholar

[2] Wang, T., He, Y.G., Luo, Q.W., Deng, F.M., Zhang, C.L. (2017). Self-powered RFID sensor tag for fault diagnosis and prognosis of transformer winding. IEEE Sensors Journal, 17 (19), 6418-6430.10.1109/JSEN.2017.2738028Search in Google Scholar

[3] Islam, M.M., Lee, G., Hettiwatte, S.N. (2017). A nearest neighbour clustering approach for incipient fault diagnosis of power transformers. Electrical Engineering, 99 (3), 1109-1119.10.1007/s00202-016-0481-3Search in Google Scholar

[4] Zhang, Y.Y., Wei, H., Liao, R.J., Wang, Y.Y., Yang, L.J., Yan, C.Y. (2017). A new support vector machine model based on improved imperialist competitive algorithm for fault diagnosis of oil-immersed transformers. Journal of Electrical Engineering & Technology, 12 (2), 830-839.10.5370/JEET.2017.12.2.830Search in Google Scholar

[5] Peimankar, A., Weddell, S.J., Jalal, T., Lapthorn, A.C. (2017). Evolutionary multi-objective fault diagnosis of power transformers. Swarm and Evolutionary Computation, 36, 2017, 62-75.10.1016/j.swevo.2017.03.005Search in Google Scholar

[6] Ding, Y., Liu, Q. (2017). Data-driven fault diagnosis method for power transformers using modified Kriging model. Mathematical Problems in Engineering, 2017, art. ID 3068548.10.1155/2017/3068548Search in Google Scholar

[7] Wei, C.H., Long, H., Yan, L. (2017). Investigate transformer fault diagnosis performance of dissolved gas analysis with measurement error. Electric Power Components and Systems, 45 (8), 894-904.10.1080/15325008.2017.1310955Search in Google Scholar

[8] Yang, Q., Su, P.Y., Chen, Y. (2017). Comparison of impulse wave and sweep frequency response analysis methods for diagnosis of transformer winding faults. Energies, 10 (4), art. no. 431.10.3390/en10040431Search in Google Scholar

[9] Li, W.L., Liu, W.J., Wu, W., Zhang, X.B., Gao, Z.H., Wu, X.H. (2016). Fault diagnosis of star-connected auto-transformer based 24-pulse rectifier. Measurement, 91, 360-370.10.1016/j.measurement.2016.05.069Search in Google Scholar

[10] Ballal, M.S., Suryawanshi, H.M., Mishra, M.K., Chaudhari, B.N. (2016). Interturn faults detection of transformers by diagnosis of neutral current. IEEE Transactions on Power Delivery, 31 (3), 1096-1105.10.1109/TPWRD.2015.2461433Search in Google Scholar

[11] Dai, C.X., Liu, Z.G., Hu, K.T., Huang, K. (2016). Fault diagnosis approach of traction transformers in high-speed railway combining kernel principal component analysis with random forest. IET Electrical Systems in Transportation, 6 (3), 202-206.10.1049/iet-est.2015.0018Search in Google Scholar

[12] Rigatos, G., Siano, P. (2016). Power transformers' condition monitoring using neural modeling and the local statistical approach to fault diagnosis. International Journal of Electrical Power & Energy Systems, 80, 150-159.10.1016/j.ijepes.2016.01.019Search in Google Scholar

[13] Illias, H.A., Chai, X.R., Abu Bakar, A. (2016). Hybrid modified evolutionary particle swarm optimisationtime varying acceleration coefficient-artificial neural network for power transformer fault diagnosis. Measurement, 90, 94-102.10.1016/j.measurement.2016.04.052Search in Google Scholar

[14] Mejia-Barron, A., Valtierra-Rodriguez, M., Granados- Lieberman, D., Olivares-Galvan, J.C., Escarela-Perez, R. (2018). The application of EMD-based methods for diagnosis of winding faults in a transformer using transient and steady state currents. Measurement, 117, 371-379.10.1016/j.measurement.2017.12.003Search in Google Scholar

[15] Wang, C., Zhang, Y.G. (2015). Fault correspondence analysis in complex electric power systems. Advances in Electrical and Computer Engineering, 15 (1), 11-16.10.4316/AECE.2015.01002Search in Google Scholar

[16] Li, Z.X., Jiang, Y., Hu, C.Q., Peng, Z.X. (2017). Difference equation based empirical mode decomposition with application to separation enhancement of multi-fault vibration signals. Journal of Difference Equations and Applications, 23 (1-2), 457-467.10.1080/10236198.2016.1254206Search in Google Scholar

[17] Li, Z.X., Jiang, Y., Hu, C., Peng, Z. (2016). Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review. Measurement, 90, 4-19.10.1016/j.measurement.2016.04.036Search in Google Scholar

[18] Glowacz, A., Glowacz, W., Glowacz, Z. (2015). Recognition of armature current of DC generator depending on rotor speed using FFT, MSAF-1 and LDA. Eksploatacja i Niezawodnosc-Maintenance and Reliability, 17 (1), 64-69.10.17531/ein.2015.1.9Search in Google Scholar

[19] Brandt, M., Kascak, S. (2016). Failure identification of induction motor using SFRA method. In 2016 ELEKTRO: 11th International Conference, 16-18 May 2016. IEEE, 269-272.10.1109/ELEKTRO.2016.7512079Search in Google Scholar

[20] Petras, J., Kurimsky, J., Balogh, J., Cimbala, R., Dzmura, J., Dolnik, B., Kolcunova, I. (2016). Thermally stimulated acoustic energy shift in transformer oil. Acta Acoustica United with Acoustica, 102 (1), 16-22.10.3813/AAA.918920Search in Google Scholar

[21] Brandt, M. (2016). Identification failure of 3 MVA furnace transformer. In Diagnostic of Electrical Machines and Insulating Systems in Electrical Engineering (DEMISEE), 20-22 June 2016. IEEE, 6-10.10.1109/DEMISEE.2016.7530472Search in Google Scholar

[22] Chen, W.G., Liu, J., Wang, Y.Y., Liang, L.M., Zhao, J.B., Yue, Y.F. (2008). The measuring method for internal temperature of power transformer based on FBG sensors. In 2008 International Conference on High Voltage Engineering and Application (ICHVE), 9-12 November 2008. IEEE, 672- 676.Search in Google Scholar

[23] Werelius, P., Ohlen, M., Adeen, L., Brynjebo, E. (2007). Measurement considerations using SFRA for condition assessment of Power Transformers. In 2008 International Conference on Condition Monitoring and Diagnosis, 21-24 April 2008. IEEE, 898-901.Search in Google Scholar

[24] Chitaliya, G.H., Joshi, S.K. (2013). Finite Element Method for designing and analysis of the transformer - A retrospective. In International Conference on Recent Trends in Power, Control and Instrumentation Engineering (PCIE 2013). Association of Computer Electronics and Electrical Engineers, 54-58.Search in Google Scholar

[25] Heathcote, M.J. (2007). The J & P Transformer Book, 13th Edition. Elsevier, ISBN 978-0-7506-8164-3.Search in Google Scholar

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