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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-57432d19-c87e-4ef7-acef-839e6b56e3fd

Czasopismo

Przegląd Elektrotechniczny

Tytuł artykułu

A new approach using Least Squares Support Vector Machines (LS-SVM) to predict Furan in power transformers

Autorzy Mahdjoubi, A.  Zegnini, B.  Belkheiri, M. 
Treść / Zawartość http://pe.org.pl/
Warianty tytułu
PL Nowa metoda prognozowania obecności Furanu w transformatorach energetycznych wykorzystująca algorytm LS-SVM
Języki publikacji EN
Abstrakty
EN LS-SVM present recently more efficiency in different industrial applications like medicine, engineering and power systems. This paper describes a methodology that was developed for the prediction of Furan in power transformers. The methodology uses as input variables such as the dissolved gases (CO and CO2). The approach presents the advantage that can reduce the time vs. laboratory tests. The validity of the approach was examined by testing several power transformers. LS-SVM gives a good estimation of results which are validated by experimental tests.
PL W artykule opisano metodologię prognozowania obecności Furanu w transformatorach energetycznych. Na wejściu podawane są takie parametry jak ilość rozpuszczonych gazów CO i CO2. Metoda opiera się na wykorzystaniu algorytmów LS-SVM.
Słowa kluczowe
PL LS-SVM   Furan   modelowanie   moc transformatora izolatorem  
EN LS SVM   Furan   modeling   power transformer insulator  
Wydawca Wydawnictwo SIGMA-NOT
Czasopismo Przegląd Elektrotechniczny
Rocznik 2014
Tom R. 90, nr 2
Strony 142--145
Opis fizyczny Bibliogr. 24 poz., tab., wykr.
Twórcy
autor Mahdjoubi, A.
  • Laboratory of studies and development of the Semiconducting and dielectric materials , Amar TELIDJI university of Laghouat, P.O box 37G, Ghardaïa road, Laghouat 03000, Algeria, mahd6875@gmail.com
autor Zegnini, B.
  • Laboratory of studies and development of the Semiconducting and dielectric materials , Amar TELIDJI university of Laghouat, bakzegni@yahoo.fr
autor Belkheiri, M.
Bibliografia
[1] Refat Atef Ghunem, Ayman H. El- Hag, Khaled Assaleh , prediction of furan content in transformer oil using Artificial Neural Networks (ANN), International symposium on electrical insulation (ISEI), IEEE 2010, pp:1-4.
[2] Ahmed E.B. Abu-Elanien, ,M.M.A. Salama, A Monte Carlo approach for calculating the thermal lifetime of transformer insulation, Electrical Power and Energy Systems 43(2012), pp: 481–487.
[3] Deepika Bhalla , Raj Kumar Bansal, Hari Om Gupta, Function analysis based rule extraction from artificial neural networks for transformer incipient fault diagnosis, Electrical Power and Energy Systems 43 (2012), pp:1196–1203.
[4] Hasmat Malik, Tarkeshwar, R.K. Jarial, Make Use of DGA to Carry Out the Transformer Oil-Immersed Paper Deterioration Condition Estimation with Fuzzy-Logic, Procedia Engineering 30 (2012), pp: 569 – 576.
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[6] A.M.Emsley,X.Xiao, R.J.Heywood and M.Ali, Degradation of cellulosic insulation in power transformers. Part 2: Formation of furan products in insulating oil, IEE Proc.-Sci. Meas. Techno, Vol 147, No. 3. May 2000 pp:110-114.
[7] Kheira Djeridane, "Contribution à la recherché des traceurs spécifiques à la dégradation du papier dans les transformateurs de puissance" , Master dissertation , Amar TELIDJI university of Laghouat- June 2012.
[8] M. Duval, A review of faults detectable by gas-in-oil analysis in transformers, IEEE Electrical Insulation Mag., 18 (2002), 8-17
[9] W. McDermid, D.H. Grant, Use of Furan-in-Oil Analysis to Determine the Condition of Oil Filled Power Transformers, 2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, April 21-24, 2008 – IEEE, pp: 479 – 481.
[10] Xiao-Yun Sun, Dong-Hui Liu, Jian-Peng Bian, The Study Of Fault Diagnosis Model Of DGA For Oil-Immersed Transformer Based On Svm Active Learning And K-L Feature Extracting, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics,Kunming,12-15 July 2008 – IEEE, pp: 1510 – 1514.
[11] XIE Hong-ling, LI Nan, LU Fang-cheng, XIE Qing, Application of LS-SVM by GA for Dissolved Gas Concentration Forecasting in PowerTransformer Oil, Power and Energy Engineering Conf., 2009. APPEEC 2009, IEEE 2009, pp: 1-4.
[12] Timothy Bosworth, Steven Setford, Richard Heywood, Selwayan Saini, Pulsed amperometric detection of furan compounds in transformer oil, Analytica Chimica Acta 450 (2001) , pp: 253–261.
[13] D. J. T. Hill, T. T. Le, M. Darvenizab, T. Sahab, A study of degradation of cellulosic insulation materials in a power transformer, part 1. Molecular weight study of cellulose insulation, Polymer Degradation and Stability 48 (1995), pp: 79-81.
[14] D. J. T. Hill, T. T. Le, M. Darvenizab, T. Saha, A study of degradation of cellulosic insulation materials in a power transformer. Part 2: tensile strength of cellulose insulation paper,Polymer Degradation and Stability 49 (1995), pp: 329-435.
[15] Xiaoyun Sun, Guoqing An, Ping Fu, Fault Diagnosis Model of Power Transformer Based on an Improved Binary Tree and the Choice of the Optimum Parameters of Multi-class SVM, Intelligent Computing and Intelligent Systems, 2009. ICIS 2009, IEEE International Conference on 2009, vol.4, pp.567-
[16] Xiaoyun Sun, Jianpeng Bian, Donghui Liu, Zhenquan Li, The Study of Transformer Fault Diagnosis Based on Means Kernel Clustering and SVM Multi-class Object Simplified Structure, Proceedings of the 7th World Congress on Intelligent Control and Automation June 25 - 27, 2008, Chongqing, China, IEEE 2009, pp:5158-5161.
[17] M. Elsamahy, M. Babiy , An Intelligent Approach using SVM to Enhance Turn-to-Turn Fault Detection in Power Transformers, IEEE-2012 pp:255-260.
[18] Li Yanqing , Huang Huaping, Li Ningyuan, Xie Qing, Lu Fangcheng, The Application of the IGA in Transformer Fault Diagnosis Based on LS-SVM, Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific , IEEE 2010, pp:1-4.
[19] Tsair-Fwu Lee1,2, Ming-Yuan Cho1, Chin-Shiuh Shieh1, and Fu-Min Fang, Particle Swarm Optimization-Based SVM Application: Power Transformers Incipient Fault Syndrome Diagnosis, 2006 International Conference on Hybrid Information Technology (ICHIT'06), IEEE 2006, pp: 468 – 472.
[20] A. Abu-Siada, Sin P. Lai, Syed M. Islam, A Novel Fuzzy-Logic Approach for Furan Estimation in Transformer Oil, IEEE Tans. on power delivery, vol. 27, no. 2, april 2012, pp:469-474.
[21] Miloš Božić, Miloš Stojanović, Zoran Stajić, and Đukan Vukić, "Power Transformer Fault Diagnosis based on Dissolved Gas Analysis with Logistic Regression," PRZEGLĄD ELEKTROTECHNICZNY, 6 (2013), 83-87
[22] J.A.K Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least squares support vector machines, World scientific publishing, Singapore, 2002.
[23] B. Zegnini, A.H Mahdjoubi, M. Belkheiri, A Least Squares Support Vector Machines (LS-SVM) Approach for Predicting Critical Flashover Voltage of Polluted Insulators, 2011 IEEE pp:403-406.
[24] Muhsin Tunay Gencoglu, Murat Uyar, Prediction of flashover voltage of insulators using least squares support vector machines, Expert Systems with Applications 36 (2009), pp: 10789–10798.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-57432d19-c87e-4ef7-acef-839e6b56e3fd
Identyfikatory
DOI 10.12915/pe.2014.02.37