Detection of Short Circuit Faults in Power Transformer by the Measurement of Its Windings Voltages and Currents Using a Neuro-Fuzzy System

Subject Areas : Renewable energy

Homayoun Meshgin Kelk ¹ , Mahyar Mohammadpour ²

1 - Department of Electrical Engineering- Tafresh University, Tafresh, Iran
2 - Department of Electrical Engineering- Tafresh University, Tafresh, Iran

Received: 2021-06-13 Accepted : 2021-08-04 Published : 2021-08-23

Keywords: power transformer, neuro-fuzzy system, winding fault,

Abstract :

Insulation failure between winding turns is one of the main causes of incipient winding fault in a transformer. During the operation of a transformer, strong electric fields are applied to the dielectric material of its windings. Dielectric deterioration and aging is one of the major cause of short circuit faults in transformer windings. Due to the probable occurrence of this type of defect and its extension, its early detection is a very important task in power systems. In this paper, it is shown that by measuring the phase difference between winding voltage and winding current of a loaded transformer, the existence of internal winding fault can be detected. For online fault detection, an intelligent system (neural-fuzzy system) has also been proposed.Both simulation results and laboratory tests confirm the ability of the proposed method for the detection of internal winding faults especially at light loads. With this method, there is no need to de-energize the power transformer.

References:

[1] CIGRE Working Group A2.37, "Transformer reliability survey", CIGRE Brochure 642; CIGRE: Paris, France, Dec. 2015.

[2] J. Chong, A. Abu-Siada, "A novel algorithm to detect internal transformer faults", Proceeding of the IEEE/­P­E­S,  pp. 1-5, Detroit, MI, USA, July 2011 (doi: 10.1109/PES.2011.6039472).

[3] D. Kweon, Y. Kim, "Interpretation of turn-to-turn insulation fault by dissolved gas analysis", IEEE Trans. on Dielectrics and Insulation Materials, vol. 25, no. 4, pp. 1560-1566, Aug. 2018 (doi: 10.1109/TDEI.2­018.0­07­4­77).

[4] H.D. Faria, J.G. Costa, "A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis", Renewable and Sustainable Energy Reviews, vol. 46, pp. 201-209, June 2015 (doi: 10.1016/j.rser.2015.02.052).

[5] S. M. Ghoneim, I.B.M. Taha, "A new approach of DGA interpretation technique for transformer fault diagnosis", International Journal of Electrical Power and Energy Systems, vol. 81, pp. 265-274, Oct. 2016 (doi: 10.1016/j.ijepes.2016.02.018).

[6] H. Mirzaei, A. Akbari, E. Gockenbach, K. Miralikhani, "Advancing new techniques for UHF PD detection and localization in power transformers in the factory tests", IEEE Trans. on Dielectrics and Electrical Insulation, vol. 22, no. 1, pp. 448-455, Feb. 2015 (doi: 10.1109/TDEI.2014.004249).

[7] S. Tenbohlen, S. Coenen, M. Djamali, A. Muller, M.H. Samimi, M. Siegel ,"Diagnostic measurements for power transformers", Energies, vol. 9, no. 5, Article Number: 347, May 2016 (doi: 10.3390/en9050347).

[8] M. Tahir, S. Tenbohlen, "Transformer winding condition assessment using feedforward artificial neural network and frequency response measurements", Energies, vol. 14, no. 11, Article Number: 3227, pp. 1-25, May 2021 (doi: 10.3390/en14113227).

[9] S. Miyazaki, M. Tahir, S. Tenbohlen, "Detection and quantitative diagnosis of axial displacement of transfo­rmer winding by frequency analysis", IET Generation, Transmission and. Distribution, vol. 13 no. 15, pp. 3493-3500, Aug. 2019 (doi: 10.1049/iet-gtd.2018.6032).

[10] S. Hajiaghasi, K. Abbaszadeh, A. Salemnia, "A new approach for transformer interturn faults detection using vibration frequency analysis", Iranian Journal of Electrical and Electronic Engineering, vol. 15, no. 1, pp. 14-28, March 2019 (doi: 10.22068/IJEEE.15.114).

[11] M. Tahir, S. Tenbholen, S. Miyazaki, "Analysis of statistical methods for assessment of power transformer frequency response measurements", IEEE Trans. on Power Delivery, vol. 36, no. 2, pp.618-626, April 2020 (doi: 10.1109/TPWRD.2020.2987205).

[12] H. Zhou, K. Hong, H. Huang, J. Zhou, "Transformer winding fault detection by vibration analysis methods", Applied Acoustics, vol. 114, pp. 136-146, Dec. 2016 (doi: 10.1016/j.apacoust.2016.07.024).

[13] C.Q. Su, "A new fuzzy logic method for transformer incipient fault diagnosis", Proceeding of the IEEE/ICFS, pp. 324-327, Vancouver, BC,  Canada, July 2016 (doi: 10.1109/FUZZ-IEEE.2016.7737704).

[14] IEC60076-18, "Measurement of frequency response", ED.1', IEC Std. March 2012, International Electr­otechnical Commission: Geneva, Switzerland, 2012.

[15] IEEE Std C57.149-2012., "IEEE giude for the application and interpretation of frequency response analysis for oil-immersed transformers", IEEE Standard Association, New York, NY, USA, 2013.

[16] L. Sevev, U. Khan, Z. Zhang, "Enhancing power transformer differential protection to improve security and dependability", IEEE Trans. on Industry Applications, vol. 53, no. 3, pp. 2642-2649, Feb. 2017 (doi: 10.110­9/TIA.­20­17.2670525).

[17] A.S. Masoum, N. Hashemnia, A. Abu-Saida, M.A.S. Masoum, S.N. Islam, "Online transformer internal fault detection based on instantaneous voltage and current measurements considering impact of harmonics", IEEE Trans. on Power Delivery, vol. 32, no. 2, pp. 587-598, Sept. 2014 (doi: 10.1109/TPWRD.2014.2358072).

[18] N. Asadi, H. Meshgin-Kelk, "Modeling, analysis, and detection of internal winding faults in power transformers", IEEE Trans. on Power Delivery, vol. 30, no. 6, pp. 2419-2426, May 2015 (doi: 10.1109/TPW­RD.20­15.2­431972).

[19] R.A Hooshmand, M. Parastegari, Z. Forghani, "Adaptive neuro-fuzzy inference system approach for simultaneous diagnosis of the type and location of faults in power transformers", IEEE Electrical Insulation Magazine, vol. 28, no. 5, pp. 32-42, Aug. 2012 (doi: 10.1109/MEI.2012.6268440).

[20] M. Maleki, "Design and implementation of a monitoring system for the detection of internal winding faults in power transformers", Msc. Thesis, Tafresh University, 2017 (in Persian).

[21] X. Yan, W. Zengping, L. Qing, "A novel inductance calculation method in power transformer model based on magnetic circuit", Proceeding of the IEEE/TENCON, pp. 1-4, Melbourne, VIC, Australia, Nov. 2005 (doi: 10.1109/TENCON.2005.301303).

[22] M. Maohammadpour, "Detection of short circuit fault in power transformer by neuro-fuzzy system", Msc. Thesis, Tafresh University, 2018 (in Persian).

[23] T.J. Ross, "Fuzzy logic with engineering applications", 3^rd Edition, University of New Mexico, USA, 2010.

[24] K. Ramesh, M.Sushama, "Inter-turn fault detection in power transformer using fuzzy logic", Proceeding of the IEEE/ICSEMR, pp. 1-5, Chennai,  India, Nov. 2014 (doi: 10.1109/ICSEMR.2014.7043581).