Virtual instrument based fault classification in power transformers using artificial neural networks

Inrush currents in power transformers are detected based on magnitude of second harmonic component. To avoid the harmful effects of inrush, amorphous core is widely used in recent days. Transformers with amorphous core cause low magnitude inrush current and hence the second harmonic of inrush current is comparable with that during internal faults. This increases the chances for relay mal operation when classical techniques of discriminating inrush from other faults are used. To overcome this, advanced signal processing techniques like wavelets, S-transform, H-transform and pattern recognition tools like fuzzy logic, neural network, support vector machine etc. are being used in recent days. A combination of wavelets and neural network is found to give satisfactory solution to the above problem. In this paper, a comparative study using different mother wavelets along with different activation function is made to enhance the performance. Virtual instrument is used to demonstrate the method of fault classification.