Sampling error-based model-free predictive current control of open-end winding induction motor with simplified vector selection

Abstract

A sampling error-based finite-set predictive current control (FS-PCC) is proposed in this article for the open-end winding induction motor (OEWIM) drive. The proposed scheme controls the zero-sequence current (ZSC) alongside the stator currents. In a model-free approach, this method predicts the future of ZSC and stator current components by the stator current and voltage sampling errors. In this way, the parameters of the OEWIM are not utilised in the prediction algorithm of the FS-PCC. So, the proposed method is robust against the variation of the parameter. Moreover, this article presents a simple vector selection technique for the FS-PCC of the OEWIM. The proposed technique has two cost functions and a simple algebraic equation to put the voltage vectors (VVs) in the prediction algorithm. The first cost function uses VVs that do not have the zero-sequence voltage component. Then, the algebraic equation determines VVs that must be utilised in the second cost function. Finally, the optimum VV is selected by the second cost function. In the proposed scheme, the prediction algorithm is iterated 14 times instead of 27 iterations of the conventional predictive algorithm. So, besides establishing a novel model-free prediction algorithm, the proposed method has almost 50% fewer calculations. The validity of the proposed sampling error-based FS-PCC and the simplified vector selection technique has been verified through experimental tests.