Optimal Voltage Vector Research Articles

A Simplified Voltage Vector Preselection-Based Multivector Predictive Current Control for Improved Torque Performance of PMSM Drive

Predictive Current Control (PCC) is a pioneering approach for constrained non-linear systems demanding dynamic performance in industrial and transportation applications. This article proposes a multivector PCC scheme based on a simplified voltage vector (VV) preselection for improved torque performance of PMSM drives. The prediction and optimization operations in the basic PCC (BPCC) scheme increase the computational burden of the processor. The proposed method aims to reduce the complexity of the control scheme with a VV-preselection based on an effective position estimation using the gradient concept. This helps to directly determine the optimal VV without evaluating cost-function using all the VVs. However, the application of a single VV in BPCC leads to large undulations in the torque and flux responses. This necessitates the application of multiple VVs in each control period to ensure improved torque response. To achieve this, the duration of application of the VVs is determined using the cost-function ratio of the different VVs chosen in the proposed scheme. This effectively reduces the steady-state torque ripples of the drive while retaining its dynamic response. The performance of the proposed scheme is evaluated through experimentation and compared comprehensively with BPCC and recent literature.

Bulanık Anahtarlama Algoritması ile DTC Kontrollü Asenkron Makine için İyileştirilmiş Tork ve Hız Performansları

Direct Torque Control (DTC) is a vector control method based on the control of the stator flux vector in the desired direction. The control of the stator flux vector is achieved by direct selection of the optimum inverter output voltage vectors. The limit values of the trajectory determined in the rotation of the stator flux vector are determined using hysteresis controllers. In this study, performance analysis of two different control methods for reducing speed and torque oscillations of a three-phase asynchronous motor controlled by direct torque control are presented. In Matlab/Simulink based simulation studies, performance analyses were made for different speed and torque references of the motor, and both the transient and steady state speed and torque changes were presented comparatively. When the obtained results are examined, it is seen that the performance of the new fuzzy-based controller, which is offered instead of the Proportional Integral Derivative (PID) controller used in traditional control, significantly decreases in the specific conditions of motor speed and torque oscillations. However, considering the control structure of the direct torque controller, the simple and plain control structure has been preserved. According to the results obtained, it has been shown that the Fuzzy Logic (FL) controller gives a better result than the PID controller at t=4.5 seconds.

KENDALI TORKA PREDIKTIF DENGAN PREDIKSI DUA LANGKAH MOTOR INDUKSI YANG DICATU OLEH SEBUAH INVERTER TIGA FASE

The predictive torque control (PTC) technique of electric motor drive is based on the principle that the determined optimum voltage vector will be applied to the motor in the next cycle. If a three-phase inverter is used, this inverter can only realize eight different switching states. One of the eight switching states is used as the optimum inverter switching state for the next cycle. PTC with one-step prediction cannot obtain an experimental motor speed control, because in the real-time implementation, there is a single sampling delay between input and optimal actuation due to a large number of real time calculations. The delay time problem could be solved by modifying the optimization steps by using the two-step prediction of PTC model. The experimental results show that the motor can follow the reference speed and command torque in the step response and variable speed experiments. In variable speed experiment, good speed and torque dynamics can be achieved by selecting the proportional gain value that corresponds to the motor speed

Effective Multivector-Operated Predictive Current Control of PMSM Drive With Reduced Torque and Flux Ripple

Model predictive current control (MPCC) is a frequently used method for the control of permanent magnet synchronous motor (PMSM). It has faster execution on modern digital platform than the previous generation micro-controllers. Conventional MPCC fails to provide satisfactory steady-state performance as a single voltage vector (VV) is applied during every sampling interval. This article proposes two novel multi-vector operated MPCC methods for reducing torque and flux ripples, as well as the computational burden. The first proposed method uses one active and one null VV; whereas, the second method uses two active and one null VV to further improve the steady-state performance. Proposed methods determine required change in stator-flux (CSF) to reduce/eliminate error in statorcurrent during every sample and VVs nearer to CSF vector are chosen as optimal VVs. The durations of optimal VVs are calculated using the components of CSF along optimal VVs. CSF, obtained using calculated durations of optimal VVs, significantly reduces torque and flux ripples. The proposed methods are effective and less complex compared to other multi-vector operated methods. The proposed methods are compared with existing two-VV and three-VV based MPCC methods and their effectiveness is experimentally verified for decreased torque and stator-flux ripple in addition to a small rise in switching frequency.

Low-Complexity Multistep Sequential Model Predictive Current Control for Three-Level Inverter-Fed Linear Induction Machines

This paper proposes a low-complexity multistep sequential model predictive current control (M-SMPCC) for three-level inverter-fed linear induction machine (LIM) drives to further improve the steady-state drive performance at low switching frequency. In order to implement multistep MPC for performance improvement, the idea of reducing the search space to fewer voltage vectors (VVs) is adopted to reduce its dramatically increasing complexity. However, there are some problems of the existing simplified methods directly applied for long prediction horizons. First, the reference VV in existing methods cannot consider the performance of long prediction horizons, so a reference VV sequence considering current tracking performance and switching frequency is derived to guide the optimal VV search. Second, with the existing search methods, some VVs that yield better performance in long prediction horizons may be excluded in advance. Hence, a more detailed region division and search method is designed. Moreover, to address the inherent neutral point voltage (NPV) fluctuation in the topology, one sequential optimization strategy is proposed, in which the current tracking performance and NPV balance are guaranteed by two separate cost functions. Finally, the proposed method has validated through comprehensive simulation and experiments, showing lower current harmonics and thrust ripples at low switching frequency.

Fault-Tolerant Predictive Control for Five-Leg Dual-Mover Permanent-Magnet Motor Drives

In this paper, a fault-tolerant predictive control (FTPC) is proposed for the post-fault operation of five-leg dual-mover primary permanent-magnet linear motor (PPMLM) drives with open circuit fault (OCF). In the post-fault operation, one phase winding and one voltage-source-inverter (VSI) leg are lost. As a result, there are four VSI legs and five healthy phase windings from two movers. Especially, one VSI leg is shared by two movers. The objects of FTPC are to reduce the computation burden and improve the performances. Firstly, A minimum copper loss principle is designed to distribute the reference current for two movers. Secondly, two global optimal mover voltage vectors (MVVs) for two movers are respectively determined by geometrical location. Thirdly, the two global optimal MVVs are respectively modified as two local optimal MVVs, in which the influence of the common leg is considered. Finally, two synthesized voltage vectors are obtained to improve the steady-state performances. Meanwhile, 60% of the computation burden can be reduced by using FTPC. The effectiveness of the proposed FTPC have been verified by experimental results.

Computationally-Efficient Model Predictive Control of Dual-Output Multilevel Converter in Hybrid Microgrid

This paper presents a hybrid microgrid configuration with a flying capacitor dual-output (FCDO) converter. The output ports of the FCDO converter can directly interface ac sources/loads operating at different amplitudes and frequencies without additional ac/dc/ac converter units. Compared to the conventional configuration, the hybrid microgrid with the FCDO converter operates at multilevel voltages, reduced power conversion stages, less power switch count, and fewer control loops. The paper also presents a cascaded model predictive control (CMPC) algorithm for such configuration to control the variables, namely three-phase dual-output currents, ac/dc bus and FC voltages, and active/reactive power. The proposed CMPC sequentially executes multiple single-objective MPC units with adaptive dynamic reference (ADR) models to control the multivariable. The controller first obtains the optimum voltage vector for each output port by minimizing the output current errors, where the ADR model generates the appropriate references. Finally, the controller identifies the optimum state from the determined voltage vector pair by minimizing the FC voltage errors. Unlike conventional MPC, the CMPC algorithm reduces the computational burden of the controller and attains multivariable control without additional control loops and weighting factors. Furthermore, the converter's performance with CMPC algorithm is validated experimentally on a low-power hybrid microgrid.

An Improved Model Predictive Torque Control for Switched Reluctance Motors With Candidate Voltage Vectors Optimization

This paper proposes an improved model predictive torque control (MPTC) strategy for switched reluctance motor (SRM) drives with candidate voltage vectors (CVVs) optimization, which can suppress the torque ripple effectively and enhance the system efficiency. This MPTC method is improved by three aspects. Firstly, the flux linkage calculation is removed compared to conventional MPTC. Secondly, the electric cycle of SRM is divided into six sectors based on the ideal torque contribution profile and the commutation region of the motor is redefined. Finally, CVVs of each sector are adopted based on phase torque characteristics and the total number of CVVs is reduced to 2 or 3 at each control period. The cost function is designed for the torque ripple suppression and copper loss minimization by selecting the optimal voltage vector from CVVs. This improved MPTC method avoids mass useless computation compared to conventional MPTC and no longer relies on hysteresis control loops compared to DTC method. In the simulation and experimental verification on a three-phase 12/8 SRM, the proposed MPTC method effectively reduces the torque ripple, improves the torque–ampere ratio and system efficiency, and improves dynamic response compared to DTC method.

Generalized Deadbeat Solution for Model Predictive Control of Five-Phase PMSM Drives

Model predictive control (MPC) has been widely emerged and applied on five-phase PMSM drives. However, the conventional MPC schemes have employed the high-complexity enumeration process and/or virtual voltage vector(s) (V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (s)), which are designed offline in one subspace. Thus, the traditional MPC schemes may have a large computational burden, low tracking accuracy, and/or limited DC-bus voltage utilization. This article proposes a generalized low-complexity deadbeat (DB) solution for MPC schemes of the five-phase drives, which have a double-space control problem. In the proposed solution, the double-space control problem of the five-phase PMSM drive is formulated in a linear system of equations. To have a unique solution, five candidate real vectors are defined and solved for their duties during every sample interval. The optimal voltage vector(s) are directly selected without any enumeration process according to the highest duty cycle(s), which are designed online considering the control objectives of the two-subspace components. Hence, five different MPC schemes can be obtained from this solution. Compared with traditional MPC schemes, the salient features of the proposed DB-based schemes are a low computational burden, improved steady-state performance, high DC-bus voltage utilization, and a simple generalized execution. These features are verified using a five-phase PMSM drive experimental prototype.

Model-Free Predictive Current and Disturbance Rejection Control of Dual Three-Phase PMSM Drives Using Optimal Virtual Vector Modulation

Model-based predictive current control (MBPCC) relies heavily on adequate system modeling and accurate parameters. The detailed models of dual three-phase permanent magnet synchronous motors (PMSMs) contain mutual cross-coupling dynamics which make it difficult to extract accurate parameters. Also, some of the physical parameters of PMSMs are generally nonlinear functions of current and rotor position. In this article, a model-free predictive current control (MFPCC) based on an ultralocal model and an extended state observer is proposed for an asymmetrical dual three-phase (ADTP) PMSM. The MFPCC method is shown to provide superior current regulation when compared to the standard MBPCC approach under uncertain parameter conditions. Furthermore, the harmonic currents and the current ripple in the ADTP PMSM drive have been regulated to near-zero values by using optimized voltage vectors comprised of virtual vectors and null vectors. A generalized center-aligned pulsewidth modulation (PWM) scheme is presented to facilitate the synthesis of the optimal virtual voltage vectors for implementation on a low-cost digital signal processing platform. The performance improvements of the MFPCC with optimal virtual vector modulation are verified and compared with the conventional MBPCC method in both simulations and experiments on a surface-mount type dual three-phase PMSM.

Computationally Efficient Model-Free Predictive Control of Zero-Sequence Current in Dual Inverter Fed Induction Motor

A model-free predictive zero-sequence current (ZSC) control is presented in this article for the dual-inverter-fed open-end winding induction motor (OEWIM). The proposed method is performed in a cascaded finite set predictive control approach to decrease the computational burden. In the proposed method, a state observer estimates the ZSC and its disturbances. The estimated ZSC is suppressed by a disturbance rejecting control loop that produces the reference of common-mode voltage (CMV). As a benefit, the motor parameters have not appeared in this process, which improves the robustness of the whole control system. To reduce the computational burden, the model-free predictive control (MFPC) is implemented in two cascaded stages. First, the predictive algorithm is performed for the voltage vectors (VVs) with zero CMV to detect the optimum zone of the space vector locations. In the second stage, the proposed MFPC is implemented with the VVs of the selected zone, and the optimum VV is achieved. In this way, the predictive algorithm is iterated 13 times. On the contrary, the conventional predictive algorithm is iterated 27 times for the VVs of the dual inverter. So, the calculation of the proposed method is reduced almost 50%. This feature is achieved without neglecting any of the VVs. Therefore, the performance of the proposed method is not degraded by reducing the calculations. A comparative analysis is carried out through simulations and experimental tests. The results confirm that while the proposed MFPC method is computationally efficient, its performance and robustness are also improved.

A DC-Offset Reduced Grid-Voltage Sensorless Model Predictive Control Strategy for Grid-Tied Inverters With a Two-Stage Optimal Voltage Vector Searching Method

Grid voltage sensorless model predictive control (MPC) strategies for grid-tied inverters (GTIs) have been studied frequently in recent years. However, the effects of DC-offset are not well addressed, and the current ripples are quite large as only one voltage vector (VV) is applied per control period. To solve these problems, a DC-offset reduced grid voltage sensorless MPC strategy for GTIs with a two-stage optimal VV searching method is proposed. First, a new grid voltage observer is proposed to reduce the effects of DC-offset. Second, by analyzing the proposed observer, it is found that the introduced intermediate variable can be used to estimate the DC-offset contained in the sampled grid current. Thus, the effects of DC-offset on the current control can be compensated accordingly. Third, to reduce the current ripples, a new two-stage optimal VV searching method is proposed on the basis of a detailed theoretical analysis. The novelty of this method is that it searches the phase and amplitude of the optimal VV in two independent stages, which reduces the number of VV that required to be evaluated online obviously. Finally, comparative experimental studies verify the effectiveness of the proposed methods in this paper.

Double-Objective Global Optimal Model-Free Predictive Control for SMPMSM Drive System Based on DSVM

To achieve the global optimization of stator current and inverter switching frequency, a double-objective global optimal model-free predictive control (MFPC) is proposed based on discrete space vector modulation (DSVM) for surface-mounted permanent magnet synchronous motor (SMPMSM) drive system. First, the double-objective invalid optimization area of the conventional DSVM-based finite control set (FCS) model predictive control (MPC) is revealed, and the reason is analyzed that the global optimal voltage vector cannot be achieved. Then, according to the distribution of the double-objective invalid optimization area, the voltage hexagon is divided into three subareas. In each subarea, the candidate voltage vector with the best current control performance (BC-CVV) is obtained. Moreover, the inverter switching number of the three BC-CVVs is used as a criterion, and the number of voltage vectors evaluated online is significantly reduced. As a result, the double-objective global optimal voltage vector is achieved without enumerating all the feasible voltage vectors. Finally, experiments under different operating conditions are implemented and the effectiveness of the proposed method is confirmed.

Research on Optimization SVPWM Method of Nine-switch Inverter for Bearingless Motor Based on Reducing Switching Times

The traditional bearingless motor system controlled by two three-phase inverters has a high cost and volume. In this paper, the nine-switch converter is applied to the bearingless motor control system, which can control the torque winding and suspension winding of the bearingless motor at the same time to save three MOSFETs. This paper proposes a nine-switch inverter SVPWM method to reduce switching loss. This method is no longer limited to only considering the action sequence of voltage vectors but reduces the switching number from selecting voltage vectors. The optimal nine-switch inverter basic voltage vector selection principle is determined by comparing the switching times of each sector combination with different voltage vector combinations. Finally, the simulation results verify the effectiveness of this method.

Model Predictive Current Control for Asymmetric Six-phase Permanent Magnet Motors With Less Computation and Optimized Voltage Vector Magnitude

For the sake of the fixed magnitude of candidate control sets, the conventional model predictive current control (MPCC) for asymmetric six-phase permanent magnet motors suffers from huge harmonic currents. This article designs a novel MPCC that aims to improve operational performance. First, virtual-voltage vector (VV) sets are synthesized as the new candidate vectors to restrain harmonic currents. Then, a simplified method for evaluating voltage vectors is used to alleviate the computation in the enumeration process. Moreover, the duty cycle modulation with the deadbeat solution is introduced to optimize the magnitude of the VV. The modulation range can be expanded by utilizing null vectors, which will significantly reduce the torque fluctuations and flux ripples. The experiments are conducted to verify the designed control strategy.

Hybrid Multi-Vector Modulated Model Predictive Control Strategy for Voltage Source Inverters Based on a New Visualization Analysis Method

Conventional model predictive control (MPC) strategy for voltage source inverters has large current ripple since only one voltage vector is applied in each control cycle. Although the multi-vector modulated MPC strategy can reduce the current ripple, its lack of theoretical basis makes its optimality questionable. To solve this problem, a new visualization analysis method is proposed in this article to evaluate the effectiveness of the conventional multi-vector modulated MPC strategy, based on which a new hybrid multi-vector modulated MPC strategy is further presented to improve the current control performance. First, the principle of the proposed visualization analysis method is introduced, based on which it is verified that the conventional multi-vector modulated MPC has better control performance than the single-vector MPC. Second, based on the proposed visualization analysis method, it is also found that both the conventional double-vector and triple-vector modulated MPC strategies cannot achieve optimal control. Thus, a new hybrid multi-vector modulated MPC strategy is further proposed. By selecting an optimal voltage vector from 18 alternative voltage vector combinations, the current control performance is improved. Detailed comparative experimental results based on experimental platform and StarSim hardware-in-the-loop platform validate the effectiveness of the proposed hybrid multi-vector modulated MPC strategy.

Adaptive Capacitor Voltage-Based Model Predictive Control for Open-Winding PMSM System With a Floating Capacitor

In this article, an adaptive capacitor voltage-based model predictive control (ACV-MPC) strategy for the open-winding permanent magnet synchronous motor system with a floating capacitor (FC-OW-PMSM) is proposed. The control precision of the conventional model predictive control (CMPC) strategy is improved by adjusting the capacitor voltage without using high switching frequency methods. The position of voltage vectors (VVs) is uncertain under different capacitor voltages. To establish a unified motion process of the uncertain VVs, the VV trajectories are analyzed. Furthermore, a novel region division scheme is proposed to locate the candidate VV trajectory. To further determine the reference capacitor voltage and select the candidate VVs from the candidate VV trajectory, the proposed scheme is analyzed in each VV trajectory. Finally, a cost function with only one capacitor voltage constraint is carried out to select the optimal VV, and thus, the weighting factors and the complicated iterative process are both avoided. Experimental studies are carried out to verify the effectiveness of the proposed strategy.

Deadbeat Predictive Control Method for 4-leg Inverters

Until now, computational burden alleviation and stability issues for the three-phase four-leg converter has not yet been thoroughly investigated. However, compared to the conventional controllers, the implementation of predictive current control approach for 3-Ø, 4-L inverter suffers a large computational burden due to its additional fourth-leg. Motivated by this fact, this article provides an alternative predictive current control implementation for 3-Ø, 4-L inverter which offers reduced computational effort to achieve similar performance as the conventional FCS-MPC and ensures the global stability of the closed-loop system. To further understand the consequences of the developed control law, theoretical stability analysis has been carried out that links Lyapunov's direct method with the closed-loop system behavior. The outcome of the theoretical stability analysis demonstrates the global stability of the overall system which is later supported by the experimental results. With the proposed method, the number of possible voltage vectors required to obtain the optimal voltage vector in each sampling interval reduces from sixteen to five and thereby simplifies the prediction process. It is also derived that the Lyapunov function-based approach actually yields to the dead-beat control, which has not been previously highlighted in the previous papers. The current work also provides experimental results for different loading conditions (balanced and unbalanced) which further demonstrates the efficacy of the proposed method.

Finite Control Set Model-Free Predictive Current Control of PMSM With Two Voltage Vectors Based on Ultralocal Model

Model-free predictive current control (MFPCC) that only needs the input and output data of the system can eliminate the influence of the motor parameters mismatch. In the conventional finite control set MFPCC (FCS-MFPCC), the delay and distortion that exist in the current variations update can affect the future current prediction and the current tracking performance. This article studies an improved current variations updating mechanism to improve the update frequency. Based on the ultralocal model, the current variations corresponding to the different voltage vectors can be updated at every control period. Then, the stator currents can be predicted with the accurate current variations, and the optimal voltage vector can be selected by minimizing the cost function without using motor parameters. Besides, a sliding mode observer is designed to observe the ultralocal model's parameters fast. Furthermore, the two voltage vectors and the optimal duration are introduced to improve the current tracking performance. Meanwhile, in order to guarantee the accuracy of the future current prediction, the current compensation strategy is studied to revise the current estimation. Finally, the effectiveness of the proposed FCS-MFPCC strategy is verified through the simulation and experimental results.

A Novel Optimal Voltage Vector Selection Control Strategy for Vienna Rectifier

At present, model predictive control (MPC) is widely used in power electronic converters. The objective function is typically utilized to select the optimal voltage vector, but the switching frequency is not fixed or too high during the selection process. Therefore, to address this issue, a novel optimal voltage vector selection control strategy for Vienna rectifier is proposed in this paper. Specifically, hysteresis link is introduced into finite control set-model predictive direct power control (FCS-MPDPC). The optimization criterion is changed from the traditional objective function tracking error minimization to the longest extension step in the hysteresis loop. As a result, the switching frequency can be reduced and the control algorithm can be simplified by redefining the optimization criteria of the rectifier output voltage vector. Moreover, according to the power control model of Vienna rectifier, three-level space vector pulse width modulation (SVPWM) is used to generate the switching function for the next cycle. Finally, an experimental platform for Vienna rectifier based on dSPACE hardware is built to verify the feasibility of the proposed strategy. Experimental results show that the control strategy can ensure the rectifier to run normally and exhibit good steady state performance.