Virtual Voltage Vector-Based Sequential Model-Free Predictive Control for Multiparalleled NPC Inverters

An improved data-driven based model predictive control for zero-sequence circulating current suppression in paralleled converters

In this paper, the switching frequency reduction in two paralleled Uninterruptible Power Supplies (UPS) is studied in order to obtain maximum global system efficiency. Typically, by reducing the switching frequency of a converter, its efficiency increases. However, when connected in parallel, UPSs are subjected to a Zero Sequence Circulating Current (ZSCC) that compromises a correct system operation and increases the losses of the global system. Experimental results show that to achieve the maximum system efficiency, the ZSCC must be effectively eliminated. Finite Control Set Model Predictive Control (FCSMPC) is used as the control scheme.

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

This work introduces the control concept of variable sampling time finite control-set model predictive control (FCS-MPC). The new control concept is introduced in theory based on a review of the conventional FCS-MPC concepts performed with a constant sampling time. Based on the partitioning of the sampling instant in multiple smaller sampling instants it is possible to optimize, besides the switching states, the switching states turn-on times. Therefore, the proposed variable sampling time FCS-MPC sets both: the switching state and the related turn-on times. To utilize the available calculation power for longer sampling instants an adaptation of the control- and prediction horizon to the sampling time is proposed. The theoretical control concepts are applied to the control of a grid connected two-level voltage-source converter where a simple L-type line-filter is used to demonstrate the control performance of the variable sampling time FCS-MPC algorithms in the laboratory environment.

In this paper, a Lyaupnov-based finite control set model predictive direct torque control for the permanent magnet synchronous machine (PMSM) is proposed. In the proposed control scheme, the finite control set prediction and the Lyapunov theory are combined to minimize the torque ripple. The eight voltage vectors of the two-level converter are utilized as a finite control set for the torque prediction of the PMSM. A cost function considering the torque error, the maximum torque per ampere operation and the current limitation is introduced. Comparing to the conventional finite control set predictive control, the dominant part of the cost function is utilized as a Lyapunov function to estimate the duty cycle of each voltage vector. An optimum voltage can be obtained by the optimum voltage vector from the eight vectors and their duty cycles. A small sampling frequency and a fixed switching frequency can be realized when compared to the conventional finite set model predictive control. In the end, the simulation and experimental results validate the performance of the proposed control scheme.

Conventional predictive control (PC) has been employed for machine drives due to good dynamic response and easy implementation. However, the disturbance caused by machine parameter mismatch is one of main barriers to its widespread application. In order to deal with this issue, this work proposes a novel model-free predictive current control (MFPCC) for surfaced permanent magnet synchronous machines (SPMSM). The contribution of this work is that a novel extended state observer (ESO) is proposed and the proposed MFPCC only utilizes the input and output knowledge of the plant without using any machine parameter in controller, which can effectively suppress the disturbances. In addition, the effect of the initial machine inductance parameter mismatch on the conventional ESO of MFPCC is analyzed in detail. The proposed MFPCC is validated and compared against three methods, namely PC without ESO, PC using ESO, and MFPCC using ESO. The experimental results have been carried out to verify the effectiveness of the proposed MFPCC.

Finite Control Set-Model Predictive Speed Control for Induction Motors with Optimal Duration

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations whilst inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

Due to the large number of candidate voltage vectors, there is heavy calculation burden of finite control set-model predictive control (FCS-MPC) in multiphase open-winding (OW) motor drive systems. Though low-complexity FCS-MPC schemes exist, they are sensitive to motor parameters. In this article, a robust FCS-MPC with high computational efficiency is proposed for the nine-phase OW-PMSM. First, based on the virtual voltage vector scheme synthesized by three active vectors, harmonic voltage components are eliminated. Then, the deadbeat current control is introduced to calculate the reference voltage vector (RVV), and the optimal voltage vector is determined by the location of the RVV, which successfully reduces the number of iterations in selecting the voltage vectors and improves the computational efficiency. In addition, in order to mitigate the sensitive of the proposed scheme to motor parameters, a parameter extraction method based on a sliding mode observer is designed to enhance the robustness of the FCS-MPC. Moreover, the corrected parameters can also be used for other calculations to achieve satisfactory steady-state performance, such as duty cycle optimization. Finally, experimental results verify the feasibility and superiority of the proposed method.

Finite control set model predictive control (FCSMPC) has become a promising method in electric drives, while facing some challenges like the complex tuning process of weighting factors. Some sequential methods aim to solve this problem, however, with poor adaptability under different working conditions. This paper proposed a revised sequential predictive torque control with high adaptability based on the previous sequential predictive controls. The proposed method adds the real-time reference value instead of the fixed nominal value to the error evaluation term. This amendment enhances the adaptability when flux reference changed sharply. The effectiveness of the proposed method is verified by simulation results.

This paper presents an innovative predictive current control (PCC) strategy based on virtual voltage vectors with optimal amplitude and phase for six-phase asymmetrical permanent magnet synchronous machines (PMSMs) with isolated neutrals. This strategy combines two virtual vectors and two zero vectors over a sampling period in order to reduce the torque ripple and enhance the tracking of references in the fundamental subspace, which are responsible for the production of flux and torque. Several simulation results are provided in order to compare the performance of the six-phase PMSM drive under the proposed control strategy with other finite control set model predictive control strategies available in the literature.

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme.

Many reduced common mode voltage (CMV) modulation methods have been proposed for three-level inverters. However, most of them suffer from large current distortion because only part of the available base vectors is utilized. The interleaved paralleling can reduce both CMV and the output current distortion, though the CMV reduction is limited and large zero-sequence circulating current (ZSCC) is inevitably caused by the phase-shifted carriers. To further reduce the CMV while maintaining smaller current distortion and ZSCC, a five-level reduced CMV (RCMV) modulation method is proposed in this article, which treats the parallel inverters as a whole. Theoretical analysis reveals that the integrated modulation provides additional base vectors that contribute to smaller total harmonic distortion (THD) of the current, and the selected vectors ensure the nearest-three-vector synthesis and half-reduced CMV. The switching sequences design and neutral point balancing strategy are also developed for dual-parallel three-level inverters, ensuring that the average circulating current is zero and the neutral point voltage is balanced, which are both critical to the proper operation of parallel inverters. Superior performance in terms of CMV, THD, and ZSCC provided by the proposed method in comparison with conventional modulation is verified by comprehensive simulation and experimental results.

Deadbeat control (DBC) has been known for decades due to its conceptual simplicity and good performance. Recently finite control set model predictive control (FCS-MPC) is emerging as a powerful strategy for control of power converters. Different from DBC, which uses a modulator such as space vector modulation (SVM), FCS-MPC directly obtained the optimal switching states of power converters by minimizing a cost function. Several improved versions of FCS-MPC have been proposed to improve the steady state performance by introducing two or three vectors during one control period. This paper investigates the inherent relationship between SVM-DBC and multiple-vector FCS-MPC. It is found that with the aim of tracking error minimization, the two kinds of predictive methods can be unified in one frame, where the vector sequence and duration in FCS-MPC can be easily obtained from SVM-DBC. In other words, under some special cases, FCS-MPC and its improved versions can be considered as special cases of SVM-DBC. This is the main contribution of this paper, because DBC and FCS-MPC are previously studied separately. The theoretical study is confirmed by the analysis on state-of-the-art DBC and FCS-MPC and supported by the selected simulation study and experimental tests from a three-phase two-level voltage source PWM rectifier.

One of the main distinctive features of multiphase machines is the appearance of new degrees of freedom ( - voltages/currents) that do not exist in their three-phase counterparts. As a direct consequence, control approaches that apply a single switching state during the sampling period cannot achieve zero average - voltage production. In direct torque control (DTC) this implies that - currents are not regulated, whereas in finite-control-set model predictive control (FCS-MPC) an enhanced - current regulation is feasible only at the expense of disturbing the flux/torque production. Aiming to avoid these shortcomings, this work makes use of the concept of synthetic/virtual voltage vectors (VVs) to nullify/limit the - voltage production in order to improve the current regulation in the secondary planes. Two strategies using two and four virtual voltage vectors (2-VV and 4-VV, respectively) are proposed and compared with the standard case that applies a single switching state. Since standard MPC has the capability to indirectly regulate - currents, the improvements with the inclusion of VVs are expected to be more significant in DTC strategies. Experimental results validate the proposed VVs and confirm the expectations through a detailed performance comparison of standard, 2-VV and 4-VV approaches for DTC and MPC strategies.