The four-switch three-phase (FSTP) inverters are known for their cost-effective advantages and minimal switching losses. However, such inverter topology's progress is lagging due to control constraints and requirements, including voltage vector limitations and parameter perturbations. To overcome the issue, this paper proposes a triple-voltage-vector model-free predictive current control (TVV-MFPCC) for FSTP inverter-fed surface permanent magnet synchronous motor (SPMSM) drives. The proposed TVV-MFPCC uses the principle of discrete-space-vector modulation (DSVM) to increase the voltage vector selections. Three primary voltage vectors, either the same or distinct, are linearly combined to yield the synthesized voltage vectors. A redundant voltage vector reduction scheme is also introduced to lessen calculations by optimally reducing the candidate voltage vectors to sixteen equivalent hybrid switching modes. To improve prediction accuracy, the TVV-MFPCC performs three different current readings and three current difference calculations in each sampling period. Experiments using a TMS320F28379D microcontroller are conducted to compare the performance of the proposed TVV-MFPCC against conventional MFPCC (C-MFPCC) and validate the scheme.INDEX TERMS Discrete-space-vector modulation, four-switch three-phase inverter, model predictive current control, model-free predictive control, triple-voltage-vector.
Model-free predictive current control (MFPCC) is a promising substitute for model predictive current control (MPCC). However, the performance of the MFPCC, to a large extent, hinges on the update frequency of its lookup table. Conventionally, the update is only performed when two successive switching states applied by the controller are identical, causing a stagnation problem to the current difference of those switching states that are not applied. To address the stagnation problem, this paper proposes a novel mechanism called synchronized current difference update for model-free predictive current control (SCDU-MFPCC). The presented scheme uses the model of permanent-magnet synchronous motor (PMSM) to construct equivalent differential stator currents corresponding to seven basic voltage vectors. To that end, current slope will be defined from the current difference of two successively applied voltage vectors. An updating factor associated with the current slope is then introduced into the prediction scheme to correct the enforced response of all switching states. This scheme is applied on every current measurement to update the stored information regardless of the successive switching states applied are distinct or not. Finally, experiments are conducted to assess the performance of the new approach using a TMS320F28379D microcontroller. Experimental results demonstrate that the proposed method substantially reduces the stagnation effect under steady-state and dynamic operations.
Model predictive current controllers (MPCCs) are widely applied in motor drive control and operations. To date, however, the presence of large current errors in conventional predictive current control remains a significant predicament, due to harmonic distortions and current ripples. Naturally, noticeable current estimation inaccuracies lead to poor performance. To improve the above situation, a modulated model predictive current controller (MMPCC) is proposed for interior permanent-magnet synchronous motors (IPMSMs) in this paper. Two successive voltage vectors will be applied in a sampling period to greatly boost the number of candidate switching modes from seven to thirteen. A cost function, which is defined as the quadratic sum of current prediction errors, is employed to find an optimal switching mode and an optimized duty ratio to be applied in the next sampling period, such that the cost value is minimal. The effectiveness of the proposed method is verified through eight experiments using a TMS320F28379D microcontroller, and performance comparisons are made against an existing MPCC. In terms of quantitative improvements made to the MPCC, the proposed MMPCC reduces its current ripple and total harmonic distortion (THD) by, on average, 27.17% and 21.84%, respectively.
A model-free predictive current control for synchronous reluctance motor drives based on triple-voltage-vector with optimized duty ratio modulation is presented in this paper. The proposed scheme introduces an effective and low complexity strategy of selecting candidate voltage vectors to largely reduce computational loadings. First, the basic voltage vectors are organized to apportion six regions, representing six composite switching modes. Each region comprises two active voltage vectors and a zero voltage vector modulated by distinct duty ratios. The scheme starts from the selection of an initial basic voltage vector corresponding to a predefined cost function. The second candidate voltage vector then follows, which is chosen from the region adjacent to the initial one. The third vector is a default zero voltage vector. As a result, voltage vectors outside the selected regions are excluded from the process. Moreover, the adjustable feature makes the optimization of duty ratios adaptive. Finally, the proposed method is experimentally put to tests under various operating conditions utilizing the synchronous reluctance motor drives. Experimental results are presented to demonstrate the effectiveness of the proposal. INDEX TERMSModel predictive current control, model-free predictive current control, optimal duty ratio modulation, synchronous reluctance motor (SynRM), triple-voltage-vector CRESTIAN A. AGUSTIN was born in Isabela, Philippines, in 1989. He received the B.S. degree in electrical engineering from Isabela State University, Ilagan City, Philippines, in 2012, the M.S. degree in engineering management from the
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.