Modeling of breakdown voltage of solid insulating material using artificial neural network

Abstract

The voids or cavities within the solid insulating material during manufacturing are potential sources of electrical trees which can lead to continuous degradation and breakdown of insulating material due to Partial Discharge (PD). To determine the suitability of use and acquire the data for the dimensioning of electrical insulation systems breakdown voltage of insulator should be determined. A major field of Artificial Neural Networks (ANN)) application is function estimation due to its some useful properties, such as, non-linearity and adaptively especially when the equation describing the function is unknown. In this project, the breakdown voltage due to PD in cavities for five insulating materials under AC conditions has been predicted as a function of different input parameters namely the thickness of the insulating sample ‘t,’ the thickness of the void ‘t1’ diameter of the void ‘d’ and relative permittivity of materials by using the ANN model. The requisite training data are obtained from experimental studies performed on a Cylinder-Plane electrode. The voids are artificially created with different measures. Detailed studies have been carried out to determine the ANN model parameters which give the best results. On completion of training, it is found that the ANN model is capable of predicting the breakdown voltage Vb = f (t, t1, d, ) very efficiently and with a small value of Mean Absolute Error. The system has been predicted using MATLAB.