

BREAKDOWN VOLTAGE TEST OF DIFFERENT SOLID INSULATING MATERIALS USING ARTIFICIAL NEURAL NETWORK MODEL
Abstract
In this paper we are presenting Artificial Neural Network (MFNN) model which has different possible inputs affecting the breakdown voltage that are working temperature, the insulating material thickness, dielectric strength of insulating material, volume resistivity of materials, dissipation factor, conductivity of materials, and the materials relative permittivity that also predicts the breakdown voltage as a function of all these inputs parameters. It is important to train the Artificial Neural Network to determine the Breakdown Voltage as close as possible. For the purpose of training of neural network model, the data which are obtained by performing the experiment are used. After the completion of training of artificial neural network model, the breakdown voltages as a function of the all these input parameters are determined. We can also predict the effect regarding thickness of insulating materials, moisture content, temperature and various environment conditions and parameters on solid insulating materials. Necessity and used of MFNN model is also discuss in this paper with taking references of different research papers. Important of back propagation technique to train MFNN is also discussing. Different causes of breakdown and detonations of different solid insulating materials also focused in this paper. Software MATLAB 2010 is used for designed, trained and tested of the ANN.
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