38 - Artificial Neural Network Applications in Power Electronics and Electrical Drives

Abstract

This chapter focuses on artificial neural network (ANN) applications in power electronics and electrical drives. The learning process of an ANN is based on the training process. One of the most widely used training techniques is the error back-propagation technique. The training process is then followed by supplying with the real input data and the ANN then produces the required output data. Artificial neural networks were also used for the estimation of the rotor speed of an induction motor together with the help of induction motor dynamic model. Though the technique gives a fairly good estimate of the speed, this technique lies more in the adaptive control area than in neural networks. The rotor flux is computed from the stator flux estimate provided by the ANN and the stator current. This particular implementation also included an ANN-based decoupler which was used for the indirect field oriented (IFO) drive. The ANN can be conveniently trained off-line with the data generated by calculation of the SVM algorithm. The ANN has inherent learning capability that can give improved precision by interpolation unlike the standard lookup table method.