Main Article Content
The importance of the research on insulator pollution has been increased considerably with the rise of the voltage of transmission lines. In order to improve understanding of the flashover phenomenon in polluted insulators, several experimental and numerical studies have been made in last years. In this paper, an artificial neural network (ANN) model was built with limited number of measurements for the prediction of the critical flashover voltage of polluted insulators. The proposed network is trained using different variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density. After training, the network can estimate the flashover voltage for different inputs. The obtained results shows that the ANN model can predict the flashover phenomenon parameters without carry out any experiment.