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Alabdullh, M.K.K., Joorabian, M., Seifossadat, S.G., Saniei, M.. (1403). A New Model for Predicting the Remaining Lifetime of Transformer Based on Data Obtained Using Machine Learning. سامانه مدیریت نشریات علمی, 12(3), 224-232. doi: 10.22098/joape.2023.11093.1830
M.K.K. Alabdullh; M. Joorabian; S.G. Seifossadat; M. Saniei. "A New Model for Predicting the Remaining Lifetime of Transformer Based on Data Obtained Using Machine Learning". سامانه مدیریت نشریات علمی, 12, 3, 1403, 224-232. doi: 10.22098/joape.2023.11093.1830
Alabdullh, M.K.K., Joorabian, M., Seifossadat, S.G., Saniei, M.. (1403). 'A New Model for Predicting the Remaining Lifetime of Transformer Based on Data Obtained Using Machine Learning', سامانه مدیریت نشریات علمی, 12(3), pp. 224-232. doi: 10.22098/joape.2023.11093.1830
Alabdullh, M.K.K., Joorabian, M., Seifossadat, S.G., Saniei, M.. A New Model for Predicting the Remaining Lifetime of Transformer Based on Data Obtained Using Machine Learning. سامانه مدیریت نشریات علمی, 1403; 12(3): 224-232. doi: 10.22098/joape.2023.11093.1830

A New Model for Predicting the Remaining Lifetime of Transformer Based on Data Obtained Using Machine Learning

Journal of Operation and Automation in Power Engineering
مقاله 5، دوره 12، شماره 3، 2024، صفحه 224-232    اصل مقاله (1.09 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.11093.1830
نویسندگان
M.K.K. Alabdullh؛ M. Joorabian* ؛ S.G. Seifossadat؛ M. Saniei
Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
چکیده
Transformers are one of the most important parts of the electric transmission and distribution networks, and their performance directly affects the reliability and stability of the grid. Maintenance and replacing the faulted transformers could be time-consuming and costly and accordingly, a solution should be proposed to prevent it. This led to studies in the field of transformer lifetime management. As a result, estimating the remaining lifetime of the transformer is a crucial part for the mentioned solution. Therefore, this paper aims to tackle this issue through employing a new algorithm to estimate the lifetime of a transformer by combining selection methods and Artificial Intelligence (AI)-based techniques. The main goal of this method is to reduce the estimation error and estimation time simultaneously. The proposed approach assesses transformers based on environmental conditions, power quality, oil quality, and dissolved gas analysis (DGA). Consideration of additional factors overcomes the disadvantage of traditional methods and gives a meticulous result. In this respect, the collected data from the power transformer of Iran and Iraq as well as regions with different conditions are employed in the studied algorithm. Several combinations of algorithms are investigated to choose the best one. Principal Component Analysis (PCA) is employed in the next step for weighing the various parameters to improve the accuracy and decrease execution time. Results show that the Bayesian neural network provides the best performance in the predicting remaining lifetime of the transformer with an accuracy about 98.4%.
کلیدواژه‌ها
Machine learning؛ Predicting؛ Transformer
مراجع
  1. Schijndel, J. Wetzer, and P.A.A.F. Wouters, “Forecasting transformer reliability,” IEEE Conf. Elec. Insul. Dielectric Phenom., pp. 577–582, 2006.
  2. IEEE guide for loading mineral-oil-immersed Transformers and Step-Voltage Regulators, pp. 1–123, 2012.
  3. IEC loading guide for oil immersed power transformers, pp. 60076–7, 2005.
  4. IIEEE Recommended Practice for Establishing LiquidImmersed and Dry-Type Power and Distribution Transformer Capability When Supplying Nonsinusoidal Load Currents, pp. 1–68, 2018.
  5. Müllerová, J. Hru˚za, J. Velek, I. Ullman, and F. Stˇríska, “Life cycle management of power transformers: results and discussion of case studies,” IEEE Trans. Dielectr. Electr. Insul., Vol. 22, No. 4, pp. 2379–2389, 2015.
  6. C. Montanari, “Aging and life models for insulation systems based on PD detection,” IEEE Trans. Dielectr. Electr. Insul., Vol. 2, No. 4, pp. 667–675, 1995.
  7. “Mineral oil-impregnated electrical equipment in service: guide to the interpretation of dissolved and free gases analysis,” IEC Stand., Vol. 44, p. 60599, 2013.
  8. K. Pradhan and T. S. Ramu, “On the estimation of elapsed life of oil-immersed power transformers,” IEEE Trans. Power Deliv., Vol. 20, No. 3, pp. 1962–1969, 2005.
  9. Jahromi, R. Piercy, S. Cress, J. Service, and W. Fan, “An approach to power transformer asset management using health index,” IEEE Electr. Insul. Mag., Vol. 25, No. 2, pp. 20–34, 2009.
  10. Naderian, S. Cress, R. Piercy, F. Wang, and J. Service, “An approach to determine the hHealth iIndex of power transformers,” in Conf. Rec. IEEE Int. Symp. Elec. Insul., pp. 192–196, 2008.
  11. G.N.S. Hernanda, A.C. Mulyana, D.A. Asfani, I.M.Y. Negara, and D. Fahmi, “Application of health index method for transformer condition assessment,” in IEEE Region 10 Conf., pp. 1–6, 2014.
  12. R. Tamma, R.A. Prasojo, and Suwarno, “High voltage power transformer condition assessment considering the health index value and its decreasing rate,” High Volt., Vol. 6, No. 2, pp. 314–327, 2021.
  13. R. Tamma, R. Azis Prasojo, and S. Suwarno, “Assessment of high voltage power transformer aging condition based on health index value considering its apparent and actual age,” in 12th Int. Conf. Informa. TechNo. Elec. Eng. (ICITEE), pp. 292–296, 2020.
  14. En-Wen and S. Bin, “Transformer health status evaluation model based on multi-feature factors,” in Int. Conf. Power Sys. Techno., pp. 1417–1422, 2014.
  15. Taengko and P. Damrongkulkamjorn, “Risk assessment for power transformers in PEA substations using health index,” in 10th Int. Conf. Elec. Eng. Electron. Computer, Telecommu. Inform. Techno., pp. 1–6, 2013.
  16. Azis Prasojo, Suwarno, N. Ulfa Maulidevi, and B. Anggoro Soedjarno, “A multiple expert consensus model for transformer assessment index weighting factor determination,” in 8th Int. Conf. Condition Monitoring Diagnosis (CMD), pp. 234–237, 2020.
  17. A. Prasojo, A. Setiawan, Suwarno, N.U. Maulidevi, and B. Anggoro Soedjarno, “Development of analytic hierarchy process technique in determining weighting factor for power transformer health index,” in 2nd Int. Conf. High Volt. Eng. Power Sys. (ICHVEPS), pp. 1–5, 2019.
  18. Behkam, A. Moradzadeh, H. Karimi, M. S. Nadery, B. Mohammadi Ivatloo, G.B. Gharehpetian, and S. Tenbohlen, “Mechanical fault types detection in transformer windings uing interpretation of frequency responses via multilayer perceptron,” J. Oper. Autom. power Eng., vol. 11, no. 1, pp. 11–21, 2022.
  19. Eskandari and S. Jalilzadeh, “Electrical load manageability factor analyses by artificial neural network training,” J. Oper. Autom. power Eng., vol. 7, no. 2, pp. 187–195, 2019.
  20. Kadim, N. Azis, J. Jasni, S. Ahmad, and M. Talib, “Transformers health index assessment based on neural-fuzzy network,” Energies, Vol. 11, No. 4, p. 710, 2018.
  21. Ibrahim, R.M. Sharkawy, H.K. Temraz, and M.M.A. Salama, "Transformer health index sensitivity analysis using neuro-fuzzy modelling," in 2nd Int. Conf. Advanced TechNo. Appli. Sci. (ICaTAS), 2017.
  22. Yahaya, N. Azis, M. Ab Kadir, J. Jasni, M. Hairi, and M. Talib, “Estimation of transformers health index based on the markov chain,” Energies, Vol. 10, No. 11, p. 1824, 2017.
  23. D. Ashkezari, Hui Ma, T.K. Saha, and C. Ekanayake, “Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers,” IEEE Trans. Dielectr. Electr. Insul., Vol. 20, No. 3, pp. 965–973, 2013.
  24. M. Islam, G. Lee, and S.N. Hettiwatte, “Application of a general regression neural network for health index calculation of power transformers,” Int. J. Electr. Power Energy Syst., Vol. 93, pp. 308–315, 2017.
  25. Li, G. Wu, H. Dong, L. Yang, and X. Zhen, “Probabilistic health index-based apparent age estimation for power transformers,” IEEE Access, Vol. 8, pp. 9692–9701, 2020.
  26. Tee, Q. Liu, and Z. Wang, “Insulation condition ranking of transformers through principal component analysis and analytic hierarchy process,” IET Gener. Transm. Distrib., Vol. 11, No. 1, pp. 110–117, 2017.
  27. I. Aizpurua, B. G. Stewart, S. D. J. McArthur, B. Lambert, J. G. Cross, and V. M. Catterson, “Improved power transformer condition monitoring under uncertainty through soft computing and probabilistic health index,” Appl. Soft Comput., Vol. 85, p. 105530, Dec. 2019, doi: 10.1016/j.asoc.2019.105530.
  28. Alqudsi and A. El-Hag, “Application of Machine Learning in Transformer Health Index Prediction,” Energies, Vol. 12, No. 14, p. 2694, 2019.
  29. I. Aizpurua, S.D.J. McArthur, B.G. Stewart, B. Lambert, J.G. Cross, and V.M. Catterson, “Adaptive power transformer lifetime predictions through machine learning and uncertainty modeling in nuclear power plants,” IEEE Trans. Ind. Electron., Vol. 66, No. 6, pp. 4726–4737, 2019.
  30. Condition Assessment of Power Transformers, CIGRE, Document 761, 2019.
  31. A. Prasojo, Suwarno, and A. Abu-Siada, “Dealing with data uncertainty for transformer insulation system health index,” IEEE Access, Vol. 9, pp. 74703–74712, 2021.
  32. A.A. F. Wouters, A. Van Schijndel, and J.M. Wetzer, “Remaining lifetime modeling of power transformers: individual assets and fleets,” IEEE Electr. Insul. Mag., Vol. 27, No. 3, pp. 45–51, 2011.
  33. A. Mackenzie, J. Crossey, A. DePablo, and W. Ferguson, “On-line monitoring and diagnostics for power transformers,” IEEE Int. Symp. Elec. Insul., pp. 1–5, 2010.
  34. Zhang, M. Dong, G. Zhang, and Z. Yan, “Investigation of return voltage measurement for the assessment of power transformers,” in Int. Conf. Condition Monitoring Diagnosis, pp. 902–905, 2008.
  35. Hong, J. Zhang, Q. Xie, S. Liang, Y. Xu, S. Li, and W. Hu, “Transformer’s condition assessment method based on combination of cloud matter element and principal component analysis,” Energy Power Eng., Vol. 09, No. 04, pp. 659–666, 2017.
  36. Fuchs and M. Masoum, Power quality in power systems and electrical machines, Elsevier Academic Press, 2008.
  37. T. Dervos, C.D. Paraskevas, P.D. Skafidas, and N. Stefanou, “Dielectric spectroscopy and gas chromatography methods applied on high-voltage transformer oils,” IEEE Trans. Dielectr. Electr. Insul., Vol. 13, No. 3, pp. 586–592, 2006.
  38. Elmoudi, M. Lehtonen, and H. Nordman, “Effect of harmonics on transformers loss of life,” in Conf. Rec. IEEE Int. Symp. Elec. Insul., pp. 408–411, 2013.
  39. Hari Mukti, F. Agung Pamuji, and B. Sofiarto Munir, “Implementation of artificial neural networks for determining power transfomer condition,”, 5th Int. Symp. Advanced Control of Indu. Processes (ADCONIP 2014), pp. 4–8, 2014.
  40. R.M. Rao and A.S. Bopardikar, Wavelet Transforms: Introduction to Theory & Applications, Longman Pub Group, 1998.
  41. T. Olkkonen, Discrete wavelet transforms-theory and applications, InTech, 2011.
  42. Nadler and E. P. Smith, Pattern recognition engineering, Wiley-Interscience, 1993.
  43. Dan Foresee, and M.T. Hagan, “Gauss-newton approximation to bayesian learning,” in Proc. of the Int. Conf. Neural Net. (ICNN’97), Vol. 3, pp. 1930–1935, 1997.
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