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Recently, model-based predictive current control (MB-PCC) has been presented as a good alternative to classical control algorithms in terms of simplicity and performance reliability. However, MB-PCC suffers from the high dependence on system parameters, which may deteriorate its performance under parameters variations. On the other hand, synchronous reluctance motors (SynRMs) are susceptible to suffer from inductances variations due to the magnetic saturation. Accordingly, in this article a new model-free predictive current control of SynRMs based on a recurrent neural network (RNN-PCC) is developed and proposed. The proposed RNN-PCC relies on the identification of the SynRM currents without considering any parameters. Simulation and experimental results show that both RNN-PCC and MB-PCC have similarly excellent dynamics, while better control performance and tracking errors can be achieved thanks to the proposed RNN-PCC. [ABSTRACT FROM AUTHOR]

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines due to their ability to operate under fault-tolerant conditions. However, the transition from pre- to post-fault control needs a reliable fault diagnostics algorithm. Accordingly, this article presents a reliable diagnostics approach that allows the detection and identification of multiple open-switches and open-phase faults in power converters fed six-phase induction motors, including adjacent and nonadjacent phases. The proposed method provides normalized numerical signatures from the measured output currents without requiring either tuning effort or detection thresholds. The reliability and robustness of the proposed algorithm are proven through experimental tests. [ABSTRACT FROM AUTHOR]

Direct model predictive control (DMPC) of permanent magnet synchronous generators with full-scale back-to-back (BTB) converters plays an important role in improving the power quality injected to the grid and to comply with grid codes. However, DMPC suffers from the excessive computational burden since all the feasible voltage vectors (VVs) of the BTB converter are used for the prediction and evaluation. Moreover, the weighting factor in the cost function may affect the control performance and tuning it is a complex process. Accordingly, benefiting from the simplicity of the direct control techniques, new direct model predictive flux and power control are proposed in this article, in combination with the direct torque and power control (DTC and DPC), respectively. By defining new DTC and DPC switching tables, only three out of the eight predictions are required to select the best VV for each converter of the BTB topology, which significantly reduces the algorithm computations. To avoid the weighting factor, the electromagnetic torque is simply converted into an equivalent stator flux and only the latter one is considered in the cost function. Experimental results prove that the execution time can be reduced by 26.9%, while the best control performance can be achieved. [ABSTRACT FROM AUTHOR]