Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
last
cannonical link button

http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-b6db2b09-0dab-49d2-8483-c7705ece047c

Czasopismo

Diagnostyka

Tytuł artykułu

Prediction of critical flashover voltage of polluted insulators under sec and rain conditions using least squares support vector machines (LS-SVM)

Autorzy Mahdjoubi, Abdelhalim  Zegnini, Boubakeur  Belkheiri, Mohammed 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN This paper describes a methodology that was developed for the prediction of the critical flashover voltage of polluted insulators under sec and rain conditions least squares support vector machines (LS-SVM) optimization. The methodology uses as input variable characteristics of the insulator such as diameter, height, creepage distance, and the number of elements on a chain of insulators. The estimation of the flashover performance of polluted insulators is based on field experience and laboratory tests are invaluable as they significantly reduce the time and labour involved in insulator design and selection. The majority of the variables to be predicted are dependent upon several independent variables. The results from this work are useful to predict the contamination severity, critical flashover voltage as a function of contamination severity, arc length, and especially to predict the flashover voltage. The validity of the approach was examined by testing several insulators with different geometries. A comparison with the Grouping Multi-Duolateration Localization (GMDL) method proves the accuracy and goodness of LS-SVM. Moreover LS-SVMs give a good estimation of results which are validated by experimental tests.
Słowa kluczowe
PL LS-SVM   przeskok   modelowanie   izolator   GMDL  
EN LS-SVM   flashover   modelling   polluted insulator   GMDL  
Wydawca Polskie Towarzystwo Diagnostyki Technicznej
Czasopismo Diagnostyka
Rocznik 2019
Tom Vol. 20, No. 1
Strony 49--54
Opis fizyczny Bibliogr. 21 poz., rys., tab.
Twórcy
autor Mahdjoubi, Abdelhalim
  • Laboratory of studies and development of the Semiconducting and Dielectric Materials, LeDMaScD Amar Telidji university of Laghouat, Laghouat (03000), Algeria, ah.mahdjoubi@lagh-univ.dz
autor Zegnini, Boubakeur
  • Laboratory of studies and development of the Semiconducting and Dielectric Materials, LeDMaScD Amar Telidji university of Laghouat, Laghouat (03000), Algeria, b.zegnini@lagh-univ.dz
autor Belkheiri, Mohammed
Bibliografia
1. Gencoglu MT, Uyar M. Prediction of flashover voltage of insulators using least squares support vector machines. Expert Systems with Applications. 2009; 36: 10789-10798.
2. Sundararajan R, Gorur RS. Effect of insulator profiles on dc flashover voltage under polluted conditions: A study using a dynamic arc model. IEEE Transaction on Dielectrics and Electrical Insulation. 1994; 1(1):124-132. https://doi.org/10.1109/94.300239
3. Rizk FAM. A criterion for AC flashover of contaminated insulators. In IEEE, (PESWPM) Power Engineering Society Winter Power Meeting, New York, NY, USA. 1971;90: 135.
4. Rizk FAM. Mathematical models for pollution flashover. ELECTRA. 1981; 78: 71-103.
5. Gerardo Montoya, Isa´ıas Ram´ırez, Jorge I. Montoya. Measuring pollution level generated on electrical insulators after a strong storm. Electric Power Systems Research. 2004; 71: 267-273.
6. Zegnini B, Mahdjoubi AH, Belkheiri M. A least squares support vectors machines (LS-SVM) approach for predicting critical flashover voltage of polluted insulators. Annual report conference on electrical insulation and dielectric phenomena (CEIDP). 2011: 403-406. https://doi.org/10.1109/CEIDP.2011.6232680
7. Belkheiri M, Zegnini B, Mahi D. Modeling of the critical flashover voltage of high voltage insulators using artificial intelligence, (JICA) Journal of intelligent Computing and Applications, Serial publications. 2009; 2(2): 137-154.
8. Shigemasa Enomoto, Kuniaki Nishioka, Noshio Higashiyama, Atsushi Sasaki. Measurement of Salt Surface Density on Polluted Insulators Using a Simple x-Ray Fluorescence Technique, ELSEVIER (IJRAI) International Journal of Radiation Applications and Instrumentation. 1992; 43(5): 615-620.
9. Tarso V, Ferreira, André D, Germano, Edson Guedes da Costa. Ultrasound and Artificial Intelligence Applied to the Pollution Estimation in Insulations. IEEE transactions on power delivery. 2012; 27(2): 583-589. https://doi.org/10.1109/TPWRD.2011.2178042
10. Osama E, Gouda1, Adel Z. El Dein. Simulation of overhead transmission line insulators under desert environments, (IET-GTD) Generation, Transmission and Distribution. 2013; 7(1): 9-13. https://doi.org/10.1049/iet-gtd.2011.0778
11. Global Insulator Group, Isolateurs pour lignes de transmission et stations de distribution à tension de 0,4 à 1150 KV, catalogue des produits, Ukraine. 2012.
12. Gencoglu MT, Cebeci M. The pollution flashover on high voltage insulators. Electric Power Systems Research. 2008; 78(11): 1914-1921. https://doi.org/10.1016/j.epsr.2008.03.019
13. Ahmad AS, Ghosh P, Ahmed S, Aljunid SAK. Assessment of esdd on high-voltage insulators using artificial neural network. Electric Power Systems Research. 2004; 72(2):131-136.
14. Zegnini B, Belkheiri M, Mahi D. Modeling fashover voltage (fov) of polluted hv insulators using artificial neural networks (anns) 2009 International Conference on Electrical and Electronics Engineering ELECO. 2009: 336-340. https://doi.org/10.1109/ELECO.2009.5355301
15. Hojae Lee, Sanghoon Lee; Yeonsoo Kim, Hakjin Chong. Grouping multi-duolateration localization using partial space information for indoor wireless sensor networks. IEEE Transactions on Consumer Electronics. 2009;55(4):1950-1958.
16. Kontargyri VT, Gialketsi AA, Tsekouras GJ, Gonos IF, Stathopulos IA. Design of an artificial neural network for the estimation of the flashover voltage on insulators. (EPSR) Electric Power Systems Research. 2007; 77:1532-1540.
17. Hadi Fattahi, Habibollah Bazdar, Applying improved artificial neural network models to evaluate drilling rate Index. Tunnelling and Underground Space Technology. (2017);70: 114-124.
18. Armando Souza Guedesa, Sidelmo Magalhães Silvaa, Braz de Jesus Cardoso Filhoa, Cláudio Alvares Conceic. Evaluation of electrical insulation in three-phase induction motorsand classification of failures using neural networks. Electric Power Systems Research. 2016; 140:263-273. https://doi.org/10.1016/j.epsr.2016.06.016
19. Asimakopoulou GE; Kontargyri VT, Tsekouras GJ, Elias CN, Asimakopoulou FE, Stathopulos IA. A fuzzy logic optimisation methodology for the estimation of the critical flashover voltage on insulators. Electric Power Systems Research. 2001; 81(2):580-588. https://doi.org/10.1016/j.epsr.2010.10.024.
20. Erenturk K. Adaptive-network-based fuzzy inference system application to estimate the flashover voltage on insulator. Instrumentation Science & Technology. 2009; 37(4):446-461.
21. Slama M, Abderrahmane Beroual El-A. Behavior of AC High Voltage Polyamide Insulators: Evolution of Leakage Current in Different Surface Conditions. Power Engineering and Electrical Engineering. 2015; 13(2): 74-80 https://doi.org/10.15598/aeee.v13i2.1145.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-b6db2b09-0dab-49d2-8483-c7705ece047c
Identyfikatory
DOI 10.29354/diag/99854