A new approach using Least Squares Support Vector Machines (LS-SVM) to predict Furan in power transformers;Nowa metoda prognozowania obecności Furanu w transformatorach energetycznych wykorzystuja{ogonek}ca algorytm LS-SVM

Przeglad Elektrotechniczny

Volumn 90, Issue 2, 2014, Pages 142-145

  Mahdjoubi, Abdelhalim ^a   Zegnini, Boubakeur ^a   Belkheiri, Mohammed ^a  

^a UNIVERSITY OF LAGHOUAT   (Algeria)

Author keywords

Furan; LS SVM; Modeling; Power transformer insulator

Indexed keywords

EID: 84893624817     PISSN: 00332097     EISSN: None     Source Type: Journal    
DOI: 10.12915/pe.2014.02.37     Document Type: Article

References (24)

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.
5. Dijana Vrsaljko, Veronika Haramija, Andela Hadzi-Skerlev, Determination of phenol, m-cresol and o-cresol in transformer oil by HPLC method, Electric Power Systems Research 93 (2012), pp: 24-31.
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 Lee, Ming-Yuan Cho, Chin-Shiuh Shieh, 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 D{stroke}ukan Vukić, "Power Transformer Fault Diagnosis based on Dissolved Gas Analysis with Logistic Regression, " PRZEGLA{ogonek}D 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.