Development of Integrated Softcomputing Approach for Stator Resistance Estimation of Three Phase Induction Motor

摘要

In this paper, an integrated Quantum inspired GA (QGA) based generalized neural network (QGA-GNN) has been developed. The QGA-GNN is used for estimation of stator resistance of a 5hp Three phase Induction Motor (3Φ I.M.) under different healthy and unhealthy working conditions. The simulation model is used to collect the set of data for estimating stator winding resistance under healthy and faulty (i.e. 10%, 20%, 30% or 40% short circuited) conditions. The motor current and motor speed are considered as input and stator resistance as output of the proposed technique. The results obtained from QGA-GNN are compared with the ANN and GNN. QGA-GNN is giving good results under different working conditions. It is found that the training epochs required in ANN is about 50,000, in GNN - about 400 epochs and in QGA-GNN it is negligible. The superiority in terms of RMSE of QGA-GNN is 0.001 in comparison to ANN which is 0.012.