Some new results on neural network approximation
Neural Networks
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The contamination of insulators with diverse substances is one of the main causes for failures on overhead distribution lines. Insulators' periodic maintenance has been proved that can reduce and prevent the outages caused by contamination. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability to handle complex non-linear functions, robustness and great efficiency, even in cases where full information for the studied problem is absent. In this paper, several ANN models were addressed to identify the insulators' contamination. Each model has been constructed using different structures, learning algorithms and transfer functions in order the best generalizing ability to be achieved. Actual input and output data, collected from the Hellenic distribution system, were used in the training, validation and testing process. A comparison among the developed neural network models was performed in order the most suitable model to be selected. Finally the selected ANN model was applied on operating voltage insulators, presenting results similar to the experimental ones.