Discrimination between PD pulse shapes using different neural network paradigms

Abstract

A comparison has been carried out on the partial discharge (PD) pulse shape recognition capabilities of neural networks, using the nearest neighbor classifier, learning vector quantization and multilayer perceptron paradigms. The PD pattern recognition capabilities were assessed on artificial cylindrical cavities of different sizes. The performance of the three neural network paradigms was found to be equivalent in all respects, with the exception of the case where a distinction was required between small cavity sizes; under those circumstances, the learning vector quantization paradigm was distinctly superior to the two other paradigms. The experimental results also demonstrated that, even with simple metallic electrode cavities, the discrimination capabilities of the three types of neural networks are not always perfect.