The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
Transactions ID Number: 52-696
Full Name: N Narmadhai
Position: Assistant Professor
Age: ON
Sex: Female
Address: Department of Electrical Engineering, Government College of Technology, Coimbatore
Country: INDIA
Tel: 2432221
Tel prefix: 0422
Fax:
E-mail address: narmadhai@gct.ac.in
Other E-mails: narms2003@yahoo.co.in
Title of the Paper: Contamination level and flashover prediction of equivalent insulator model based on the characteristics of leakage current using ann
Authors as they appear in the Paper: N.Narmadhai, V.Vijeesh, N.Devarajan, A.Ebenezer Jeyakumar
Email addresses of all the authors: narmadhai@gct.ac.in, vijeesh_vj@yahoo.co.in, devarajan@gct.ac.in, ebeyjkumar@rediffmail.com
Number of paper pages: 10
Abstract: Abstract: The phenomenon of flashover in polluted insulators has been continued by the study of the characteristics of contaminating layers deposited on the surface of insulators in high voltage laboratories. In the literature, Experimental investigations have been carried out on a real insulator or a flat plate model of insulators under high voltage application.This paper proposed the Equivalent insulator trough model for studying the flashover phenomena due to pollution under wet conditions even at low voltage. Laboratory based tests were carried out on the model under AC voltage at different pollution levels. Different concentrations of salt solution has been prepared using sodium chloride, Kaolin and distilled water representing the various contaminations. Leakage current during the experimental studies were measured for various polluted conditions. A new model of Vc = f( V,Iinitial Imean, Imax and Ió) based on artificial neural network has been developed to pr!
edict flashover from the analysis of leakage current. The input variable to the artificial neural network are mean (Imean), Maximum(Imax) and standard deviation(Ió) of leakage current are extracted along with the initial value of leakage current and the input voltage(V).The target obtained was used to evaluate the performance of the neural network model. The optimum process has been carried out based on the training accuracy measured by RMSE, the network converged to a threshold of 0.0001.The trained model prediction is in good agreement with the actual results and the R value of the developed model is 0.99997 for training and0.99994 for testing. The developed ANN model is well-suited for the analysis of leakage current to predict contamination level that estimate ESDD and approaching flashover on the trough surface with high accuracy.
Keywords: Flashover, Insulator model, Leakage Current,Artificial neural network,Esdd,Mse,Rmse
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How Did you learn about congress: Control of power systems, Modelling and simulation, Predictive control, Artificial neural network,Fault detection,Real time control
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