Development of a pedagogical framework to analyze the performance of induction machines. Issue 2 (April 2021) - Sridevi, R; Kumar, C; Suresh, P; Ganesan, S; Subramanian, S; Suresh, A
This paper elucidates a procedure for ascertaining the performance of induction generators to disseminate the inevitable role of an induction machine in an alternate energy conversion among undergraduate and postgraduate students. Initially, a circle diagram is constructed for generator mode and motor mode using a drawing tool—AutoCAD from a simplified, energy consumption predetermination test data. Later a set of data is derived and is trained using an innovative, powerful pedagogical tool-radial basis function neural network to predict the performance indices of an induction machine. The proposed technique is compared with the experimental results of a three-phase 5 HP induction machine for various loaded conditions. Further, it provides encouraging results that will help the manufacturers to become more involved in the performance strategies of induction machines. Thus, the noninvasive method avoids the manipulation of an algebraic equation of nth order polynomial equations in predicting the generator performance indices.