This paper attempts to apply artificial intelligent techniques in high voltage applications and especially to estimate the critical flashover voltage (FOV) for polluted insulators, using experimental measurements carried out in an insulator test station according to the IEC norm and a mathematical model based on the characteristics of the insulator: the diameter, the height, the creepage distance, the form factor and the equivalent salt deposit density and estimates the critical flashover voltage. Two types of artificial neural networks (ANNs) are designed to establish a nonlinear model between the above mentioned characteristics and the critical flashover voltage. The ANNs models, algorithms, and tools have been developed using the software package Matlab. The obtained results are promising and insure that artificial intelligent techniques can estimate the critical flashover voltage for new designed insulators with different operating conditions and constitute an indispensable models that can be used in field simulations of various parameters for polluted insulators. Further comparative analysis of the estimated results with the measured data collected from the site measurement amply demonstrate the effectiveness of the use of Artificial intelligent techniques for modeling (ANNs) of FOV.
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