CLASSIFICATION OF DIFFERENT FAULTS IN STATOR OF AN ALTERNATOR

Synchronous generator are important elements of power system. Its reliability and proper functioning are crucial in maintaining an uninterrupted power supply to the customers. Their reliability affects the electric energy availability of the supplied area. Hence the alternator protection is critical issue in power system as issue lies in the accurate and rapid discrimination of healthy condition from different faults. It is very difficult to describe the relationship of fault information and terminal parameters by accurate mathematical expression. By applying artificial neural network in alternator, the fault diagnosis can obtain a good result. When there are faulty samples in the training samples of ANN, the severity information of the alternator faults can be directly obtained. This project describes a novel and simple artificial neural networks (ANNs) technique without using rigorous mathematics. In this project various faults were conducted on an laboratory alternator and fault currents were captured using Data Acquisition System. The energies of these current samples were calculated using Discrete Wavelet Transform and were given as input to ANN. The results so obtained are finally compared to classify the faults.