Application des fonctions Kernels de la méthode LS-SVM pour le diagnostic d’un isolateur HT pollué

This work presents a method to predict the polluted level of the surfaces of an insulator, that is to say, to diagnose the operational conditions of the isolation of an electrical system by pattern recognition techniques using some types of methods such as Least square support vectors machines (LS-SVM); we present here several kernel functions like RBF, polykernel and MLP. The methodology is to use as input variables of the insulation such as diameter, height, creepage line, form factor and equivalent salt deposition density. The majority of the variables to be predicted are dependent on several independent variables. The results of this work are useful in predicting the severity of contamination, the critical overvoltage; arc length and especially affects the overvoltage. The validity of the approach was examined by testing several insulators with different geometries. Field experience and laboratory tests are expensive both in time and money; therefore this method takes efficiency vs experimental tests in laboratories. A comparison of the kernel functions used shows the improvement of LS-SVM with RBF, Polykernels and that the use of combined models is a powerful technique for this type of application demand

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

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