We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Novel ANFIS based Algorithm for Fault Detection in Power Transformers

by Amrinder Kaur, Yadwinder Singh Brar, Leena G.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 12
Year of Publication: 2018
Authors: Amrinder Kaur, Yadwinder Singh Brar, Leena G.
10.5120/ijca2018916142

Amrinder Kaur, Yadwinder Singh Brar, Leena G. . A Novel ANFIS based Algorithm for Fault Detection in Power Transformers. International Journal of Computer Applications. 179, 12 ( Jan 2018), 34-41. DOI=10.5120/ijca2018916142

@article{ 10.5120/ijca2018916142,
author = { Amrinder Kaur, Yadwinder Singh Brar, Leena G. },
title = { A Novel ANFIS based Algorithm for Fault Detection in Power Transformers },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 12 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number12/28854-2018916142/ },
doi = { 10.5120/ijca2018916142 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:11.652239+05:30
%A Amrinder Kaur
%A Yadwinder Singh Brar
%A Leena G.
%T A Novel ANFIS based Algorithm for Fault Detection in Power Transformers
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 12
%P 34-41
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Power Transformers are important parts of the power distribution network, used to change voltages level for transmission between sub stations and electricity consumers. Power Transformers are prone to failure due to general ageing of parts, which can lead to high replacement costs. It not only interrupts the supply of electricity, but can cause explosion and potentially halting the power network. Dissolved Gas Analysis (DGA) is a reliable technique for detecting the presence of incipient fault conditions in oil immersed transformers in which the presence of certain key gases is monitored. In this paper a novel algorithm based on ANFIS (Adaptive Neural And Fuzzy Inference System) using different fuzzy membership function is proposed and it is validated on DGA data obtained from PSTCL (Punjab State Transmission Corporation Ltd.) located at Patiala & Ludhiana. The key gases considered are hydrogen, methane, ethane, ethylene, acetylene..

References
  1. IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers, IEEE Standard C57-104TM -2008,
  2. Mineral Oil Impregnated Equipments in Service—Guide to the Interpretation of Dissolved and Free Gases Analysis, IEC Publication 60599, Mar. 1999.
  3. IEEE Guide for the Interpretation of Gases Generated in Oil-Immersed Transformers (2009), IEEE Standard C57.104,.
  4. Dornenburg E, Strittmatter W(1974). “Monitoring oil-cooled transformers by gas analysis”. , 61:238–247.
  5. IEEE guide for the Interpretation of Gases Generated in Oil-Immersed Transformers (1991), ANSI/IEEE std.C57.104,.
  6. J. J. Kelly(1980), “Transformer fault diagnosis by Dissolved-Gas Analysis”, IEEE Trans. Industry Applications, Vol. 16, No. 6, pp.777-782,.
  7. M. Duval and A. DePabla(2001), “Interpretation of gas-in-oil analysis using new IEC Publication 60599 and IEC TC 10 Databases,” IEEE Electr.Insul.Mag., 7:31–41,.
  8. Jyh-Shing Roger Jang(1993), "ANFIS-Adaptive –network-based fuzzy inference system ", IEEE Trans. on Systems, Man and Cybernetics Vol. 23, No.3, pp 665-685.
  9. M. Duval (2008), “The duval triangle for load tap changers, non-mineral oils and low temperature faults in transformers”, IEEE Electr.Insul.Mag. 24 No. 6, pp. 22-29.
  10. Hasmat Malik, Surinder Singh, Mantosh Kr, R.K. Jarial,(2011) “ UV/VIS Response Based Fuzzy Logic for Health Assessment of Transformer Oil”, Elsevier/Science Direct Procedia Engineering, 30: 932-939.
  11. Li-Jung Chen, Ta-PengTsao, Yu-Hsun Lin Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan(2005), “New diagnosis approach to epoxy resin transformer partial discharge using acoustic technology, “IEEE Transactions on Power Delivery pp. 2501 – 2508
  12. Tapsi Nagpal , Yadwinder Singh Brar(2014),” Artificial neural network approaches for fault classification: comparison and performance”, Neural Comput & Applic DOI 10.1007/s00521-014-1677-y
  13. .Ivan N. da Silva, Carlos G. Gonzales, Rogério A. Flauzino, Paulo G. da Silva Junior, Ricardo A. S. Fernandes, Erasmo S. Neto, Danilo H. Spatti and José A. C. Ulson(2012), “Intelligent Systems for the Detection of Internal Faults in Power Transmission Transformers”, “Advances in Expert Systems,” Edited by PetricaVizureanu, ISBN 978-953-51-0888-7 ,Publisher: InTech,
  14. Mohammadi, E. ;Niroomand, M. ; Rezaeian, M. ; Amini, Z.(2009) , “Partial discharge localization and classification using acoustic emission analysis in power transformer”, Telecommunications Energy Conference,. INTELEC 2009.E-ISBN : 978-1-4244-2491-7 Print ISBN: 978-1-4244-2490-0
  15. Cristina CIULAVU, Elena HELEREA(2008)., “Power Transformer Incipient Fault Monitering”, Annals of the University of Craiova, Electrical Engineering series, No. 32
  16. Hasmat Malik, Amit Kumar Yadav, Sukumar Mishra, TarkeshwarMehto(2013), “Application of neuro-fuzzy scheme to investigate the winding insulation paper deterioration in oil-immersed power transformer”, Electrical Power and Energy Systems 53,pp. 256–271,
  17. Hongsheng Su(2007), “An ANFIS-based Transformer Insulation Fault Diagnosis Method Using Emotional Learning”, Published in: • Proceeding ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01, pp. 74-78 ISBN:0-7695-2875-9
  18. Hasmat Malik, Tarkeshwar , R.K. Jarial (2011), “An Expert System for Incipient Fault Diagnosis and Condition Assessment in Transformers”, International Conference on Computational Intelligence and Communication Networks Pages 138-142, ISBN: 978-0-7695-4587-5
  19. Michel Duval IREQ, Canada, “ A Review of Faults Detectable by Gas-in-Oil Analysis in Transformers”
  20. Huo-ChingSun ,Yann-Chang Huang,Chao-Ming Huang, (2012)“A review of dissolved gas Analysis in transformer”, Energy Procidia 14 pp. 1220 1225
  21. Huo-ChingSun, Yann-Chang Huang, Chao-Ming Huang,(2012) “ Fault Diagnosis of Power transformer Using Computational Intelligence: A Review”, Energy Procidia14 (2012) 1226 -1231.
  22. Andrew K.S. Jardine ,Daming Lin, DraganBanjevic, (2006)“ A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing 20: 1483–1510
  23. Verma P, Singh J, Sood YR (2012) The influence of service aging on transformer insulating oil parameters. IEEE Trans Dielectr ElectrInsul 19:421–426
  24. Bhalla D, Bansal RK, Gupta HO, Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis.Int J Electr Power Energy Syst 43:1196–1203
  25. Ozgonenel O, Kilic E, Khan MA, Rahman MA (2008) A new method for fault detection and identification of incipient faults in power transformers. Electric Power ComponSyst 36:1226–1244
  26. . Knapp GM, Jovadpour R, His-Pin Wang (2000) An ARTMAP neural network: based machine condition monitoring system. J QualMaint Eng6:86–105
  27. Yadaiah N, Ravi N(2011) , ’Internal fault detection techniques forpower transformers’. Application of Soft Computing, 11:5259–5269
  28. Karimollah Hajian-Tilaki(2013),” Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation” Caspian J Intern Med. Spring; 4(2): 627–635.
  29. Gardner MJ, Altman DG(1989). “Calculating confidence intervals for proportions and their differences”. In: Gardner MJ,Altman DG, eds. Statistics with confidence. London: BMJ Publishing Group,:28-33
  30. Wen Zhu, Nancy Zeng, Ning Wang(2010),” Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS® Implementations” NESUG
Index Terms

Computer Science
Information Sciences

Keywords

Power Transformer DGA ANFIS Fault Diagnosis.

Powered by PhDFocusTM