Abstract
Problem statement: 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 study proposed the Equivalent insulator flat plate 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. Approach: A new model of Vc = f (V, Iinitial, Iem, Iemax and Iσ) based on artificial neural network has been developed to predict flashover from the analysis of leakage current. The input variable to the artificial neural network are mean (Imean), Maximum(Imax) and standard deviation(Iσ) of leakage current extracted along with the initial value of leakage current Iinitial and the input voltage(V).The target obtained was used to evaluate the performance of the neural network model. Results: 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 R2 value of the developed model is 0.99996. Conclusion: The developed ANN model is well-suited for the analysis of leakage current to predict flashover on the insulator surface with high accuracy.
Highlights
Insulators used in outdoor electric power transmission lines are exposed to outdoor environmental contaminations
The dry contaminant layer becomes conductive when exposed to light rain or morning dews
The increase in leakage current dries the conducting layer and forms the dry bands around the areas with high current density. These dry bands interrupt the current flow and most of the applied voltages are impressed across these narrow dry bands
Summary
Insulators used in outdoor electric power transmission lines are exposed to outdoor environmental contaminations. Neural network model for predicting flashover based on the characteristics of leakage current: Neural network: ANN is a computer model representing the biological brain It consists of a set of interconnected simple processing units (neurons or nodes) bonded with weight connections which combine to output a signal to solve a certain problem based on the input signals it received. Characteristics of leakage current: The input parameters selected for the artificial neural network for flashover prediction were the three characteristics extracted from the measured leakage current are mean value of leakage current (Iem), Maximum value of the leakage current (Iemax) and Standard deviation of leakage current (Iσ) along with the initial value of leakage current (Iinitial) and the input supply voltage. Input neurons are used, three to indicate the leakage current characteristics,
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