Neural networks to analyze surface tracking on solid insulators

Abstract

Surface tracking on solid insulators is one of the most severe breakdown mechanisms associated with polymeric materials under long term service conditions. A wide range of relays can detect failure in a transmission line and prevent a total breakdown in the systems, but due to the non-healing characteristics of solid insulators, in most cases it might be too late to save the insulator after tracking initiation and growth. The method described here is employed mainly in detecting several conditions, such as discharges, leakage current, dry conditions, severe damage and tracking initiation. Initially a BPN (back propagation network) type NN (neural network) is trained with different signal types. Due to the nature of NN, which always require similar values of input nodes, the system uses the FFT (fast Fourier transform) of the input signal, which might have high amplitude frequency components other than the fundamental frequency depending on the condition of the surface. The system works on a real time basis and warns the user with the first indication of severe damage on the surface and can protect the insulator from excessive damage.