AN INTELLIGENT TECHNIQUE FOR THE HEALTH ASSESSMENT OF POWER TRANSFORMER USING THERMAL IMAGING

Irshad, Zainul A. Jaffery, Nadeem Ahmad, and Ashwani K. Dubey

References

  1. [1] M.S. Ballal, G.C. Jaiswal, D.R. Tutkane, P.A. Venikar, M.K.Mishra, and H.M. Suryawanshi, Online condition monitoringsystem for substation and service transformers, IET ElectricPower Applications, 11(7), 2017, 1187–1195.
  2. [2] CIGRE Working Group, An international survey of failuresin large power transformers in service, Electra, 88(5), 1983,21–48.
  3. [3] A. Abu Siada, M. Arshad, and S. Islam, Fuzzy logic approachto identify transformer criticality using dissolved gas analysis,IEEE Power and Energy Society General Meeting, 2010, 1–5.
  4. [4] A.E.B. Abu Elanien, M.M.A. Salama, and M. Ibrahim, Cal-culation of a health index for oil immersed transformers ratedunder 69 kV using fuzzy logic, IEEE Transaction on Dielectricand Electrical Insulation, 27(4), 2012, 2029–2036.
  5. [5] K. Ibrahim, R.M. Sharkawy, H.K. Temraj, and M.M.A. Salama,Selection criteria for oil transformer measurements to calculatethe health index, IEEE Transaction on Dielectric and ElectricalInsulation, 23(6), 2016, 3397–3404.
  6. [6] K.N. Birlik, O. Ozgonenel, and S. Karagul, Transformer healthindex estimation using artificial neural network, NationalConf. on Electrical, Electronics and Biomedical Engineering(ELECO), Bursa, Turkey, 2016.
  7. [7] A.D. Ashkezari, H. Ma, C. Ekanayake, and T.K. Saha, Mul-tivariate analysis for correlations among different transformeroil parameters to determine transformer health index, IEEEPower and Energy Society General Meeting, San Diego, CA,2012.
  8. [8] M.S. Ballal, H.M. Suryawanshi, M.K. Mishra, and B. Nemic-hand, Interturn faults detection of transformers by diagnosis ofneutral current, IEEE Transactions on Power Delivery, 31(1),2016, 1096–1105.
  9. [9] P. Venikar, M. Ballal, B. Umre, et al., A novel offline toonline approach to detect transformer interturn fault, IEEETransactions on Power Delivery, 31(2), 2016, 482–492.
  10. [10] V. Behjat and A. Vahedi, Numerical modelling of transform-ers interturn faults and characterising the faulty transformerbehaviour under various faults and operating conditions, IETElectrical Power Applications, 5(5), 2011, 415–431.
  11. [11] H. Wang and K.L. Butler, Finite element analysis of internalwinding faults in distribution transformers, IEEE Transactionson Power Delivery, 16(3), 2001, 422–428.
  12. [12] S. Liu, Z. Liu, and O.A. Mohammed, FE-based modelingof single phase distribution transformers with winding shortcircuit faults, IEEE Transactions on Magnetics, 43(4), 2007,1841–1844.
  13. [13] L.M.R. Oliveira and A.J. Marques Cardoso, A permeance-basedtransformer model and its application to winding interturnarcing fault studies, IEEE Transactions on Power Delivery,25(3), 2010, 1589–1598.
  14. [14] H.R. Mirzaei, A. Akbari, E. Gockenbach, and K. Miralikhani,Advancing new techniques for UHF PD detection and local-ization in the power transformers in the factory tests, IEEETransaction on Dielectric and Electrical Insulation, 22(1),2015, 448–455.
  15. [15] S. Bagavathiappan, B.B. Lahiri, T. Saravanan, J. Philip, and T.Jayakumar, Infrared thermography for condition monitoring:A review, Infrared Physics and Technology, 60, 2013, 35–55.55
  16. [16] R. Gade and T.B. Moeslund, Thermal cameras and applica-tions: A survey, Machine Vision Applications, 25(1), 2013,245–262.
  17. [17] P.S. Karvelis, G. Georgoulas, C.D. Stylios et al., An automatedthermographic image segmentation method for induction motorfault diagnosis, Proc. 40th Annual Conf. on IEEE Industrialand Electronics Society, Dallas, TX, USA, 2014, 3396–3402.
  18. [18] D.L. Perez and J.A. Daviu, Application of infrared thermog-raphy to failure detection in industrial induction motors: Casestories, IEEE Transactions on Industry Applications, 53(3),2017, 1901–1908.
  19. [19] M.S. Jadin and S. Taib, Recent progress in diagnosing the reli-ability of electrical equipment by using infrared thermography,Infrared Physics and Technology, 55(4), 2012, 236–245.
  20. [20] N.Y. Utami, Y. Tamsir, A. Pharmatrisanti, H. Gumilang, B.Cahyono, and R. Siregar, Evaluation condition of transformerbased on infrared thermography results, IEEE 9th InternationalConf. on Properties and Applications of Dielectric Materials,Harbin, China, 2009, 1055–1058.
  21. [21] M. Weiping, C. Fangxiao, S. Ying, X. Chungui, and A. Ming,Fault diagnosis on power transformers using the non-electricmethod, Proc. 2006 International Conf. on Power System andTechnology, Montreal, Que., Canada, 2006, 1–5.
  22. [22] J.C. Pearson and D.A. Pandya, Utilizing infrared and powerquality techniques to diagnose and re-commission 33 old power,lighting and receptacles panels and distribution transformers atthe New Jersey international and bulk mail center, Proceedingsof Electrical Insulation Conf. and Electrical ManufacturingExpo, Nashville, TN, USA, 2007, 1–4.
  23. [23] M. Manana, A. Arroyo, A. Ortiz, C.J. Renedo, S. Perez, andF. Delgado, Field winding fault diagnosis in dc motors duringmanufacturing using thermal monitoring, Applied ThermalEngineering, 31(5), 2011, 978–983.
  24. [24] G. George, R. Mathews, S. Shelly, and S. Philipose, A surveyon various median filtering techniques for removal of impulsenoise from digital image, Conf. on Emerging Devices and SmartSystems (ICEDSS), Tiruchengode, India, 2018.
  25. [25] M. Jain and A.P. Dimri, Efficacy of filtering techniques in im-proving Landsat SLC-off thermal infrared data, IEEE Journalof Selected Topics in Applied Earth Observations and RemoteSensing, 11(1), 2018, 271–284.
  26. [26] M.D. Yang, T.C. Su, and H.Y. Lin, Fusion of infrared ther-mal image and visible image for 3D thermal model recon-struction using smartphone sensors, Sensors, 18(7), 2018.doi:10.3390/s18072003
  27. [27] I. Moira and S.J.P. Heather, A review of image fusion technologyin 2005, Thermosense XXVII, Proceedings of SPIE Vol. 5782,SPIE, Bellingham, WA, 2005.
  28. [28] J. Ma, Y. Ma, and C. Li, Infrared and visible image fusionmethods and applications: A survey, Information Fusion, 45,2019, 153–178.
  29. [29] D. Bulanon, T. Burks, and V. Alchanatis, Image fusion ofvisible and thermal images for fruit detection, BiosystemEngineering, 103(1), 2009, 12–22.
  30. [30] Y. Niu, S. Xu, L. Wu, and W. Hu, Airborne infrared andvisible image fusion for target perception based on target regionsegmentation and discrete wavelet transform, MathematicalProblems in Engineering, 2012, 2012, 732–748.
  31. [31] B.B. Lahiri, S. Bagavathiappan, P.R. Reshmi, J. Philip, T.Jayakumar, and B. Raj, Quantification of defects in compositesand rubber materials using active thermography, InfraredPhysics & Technology, 55, 2012, 191–199.
  32. [32] Y.C. Chieh and L. Yao, Automatic diagnostic system of electri-cal equipment using infrared thermography, Proceedings of In-ternational Conf. on Soft Computing and Pattern Recognition,Malacca, Malaysia, 2009, 155–160.
  33. [33] L. Baoshu, Z. Xiaohui, Z. Shutao, and N. Wendong, HV powerequipment diagnosis based on infrared imaging analyzing,Proceedings of the International Conf. on the Power SystemTechnology, Chongqing, China, 2006.
  34. [34] C.A.L. Almeida, A.P. Braga, S. Nascimento, V. Paiva, H.J.A.Martins, R. Torres, W.M. Caminhas, Intelligent thermographicdiagnostic applied to surge arresters: A new approach, IEEETransactions on Power Delivery, 24(2), 2009, 751–757.
  35. [35] C. Cong-ping, Q. Wu, F. Zi-fan, and Z. Yi, Infrared imagetransition region extraction and segmentation based on localdefinition cluster complexity, International Conf. on ComputerApplication and System Modeling (ICCASM 2010), Taiyuan,China, 2010.
  36. [36] P. Wang and X. Bai, Thermal infrared pedestrian segmentationbased on conditional GAN, IEEE Transactions on ImageProcessing, 28(12), 2019, 6007–6021.
  37. [37] B. Gao, X. Li, W.L. Woo, and G. Yun Tian, Physics-basedimage segmentation using first order statistical properties andgenetic algorithm for inductive thermography imaging, IEEETransactions on Image Processing, 27(5), 2018, 2160–2175.
  38. [38] Irshad and Z.A. Jaffery, Performance comparison of imagesegmentation techniques for Infrared images, 2015 AnnualIEEE India Conf. (INDICON), New Delhi, 2015, 1–5, doi:10.1109/INDICON.2015.7443391.
  39. [39] A.S. Sajadi and S.H. Sabzpoushan, A new seeded regiongrowing technique for retinal blood vessels extraction, Journalof Medical Signals and Sensors, 4(3), 2014, 223–230.
  40. [40] H. Chen, H. Ding, X. He, and H. Zhuang, Color imagesegmentation based on seeded region growing with Cannyedge detection, IEEE International Conf. on Signal Processing(ICSP), Hangzhou, China, 2014.
  41. [41] M. Tamilarasi and K. Duraiswamy, Genetic based fuzzy seededregion growing segmentation for diabetic retinopathy images,IEEE International Conf. on Computer Communication andInformatics, Coimbatore, India, 2013.
  42. [42] J. Song, C. Yang, L. Fan, et al., Lung lesion extraction usinga toboggan based growing automatic segmentation approach,IEEE Transactions on Medical Imaging, 35(1), 2016, 337–353.
  43. [43] ANSI/NETA ATS-2009, NETA guidelines: Standard for ac-ceptance testing for electrical power equipment and systems,2009.
  44. [44] A.T.P. So, W.L. Chan, C.T. Tse, and K.K. Lee, Fuzzy logicbased automatic diagnosis of power apparatus by infraredimaging, Proceedings of the Second International Forum onApplications of Neural Networks to Power Systems, Yokohama,Japan, Japan, 1993.
  45. [45] Z.A. Jaffery, A.K. Dubey, Irshad, and A. Haque, Schemefor predictive fault diagnosis in photo-voltaic modules usingthermal imaging, Infrared Physics & Technology, 83, 2017,182–187.
  46. [46] C.O.H Morales and J.P.N. Gonz´alez, Fault detection and diag-nosis of electrical networks using a fuzzy system and Euclidiandistance, Advances in Soft Computing and Its Applications,Xalapa, Maxico, 2013, 216–224.
  47. [47] D.M. Said, K.M. Nor, and M.S. Majid, Analysis of distributiontransformer losses and life expectancy using measured harmonicdata, IEEE ICHQP 2010, Bergamo, Italy, 2010.

Important Links:

Go Back