Study on Three Phase Power Quality System Events Based on Artificial Neural Network

In this paper one of the very popular soft computing techniques called artificial neural network is employed to diagnose the stator inter turn short-circuit fault in a three phase squirrel cage induction machine. Firstly, a artificial neural network (ANN) has been applied for solving the above fault diagnosis problem. In order to apply multilayer perception artificial neural network for fault diagnosis, an induction machine in the lab is considered. Three phase variable AC voltage is applied to induction machine through a three phase variac and the stator line voltage and stator currents were measured for both healthy and faulted motor. Then an artificial neural network was developed with 3 layers namely input, hidden and output layer with 2 nodes in input and output layer whereas four nodes in the hidden layer. Using the stator line voltage and stator currents, back propagation algorithm is employed to train the said ANN. The root mean square error was plotted and the least value was found to be 0.065. In view of improving the training performance, a radial basis function neural network (RBFNN) with the same configuration as that of back propagation algorithm and the results of both the artificial neural networks based fault diagnosis approaches applied to the induction machine.