Discrete Space Vector Modulation Research Articles

Low Complexity Model Predictive Flux Control Based on Discrete Space Vector Modulation and Optimal Switching Sequence for Induction Motors

In this paper, a low-computational burden model predictive flux control (MPFC) based on discrete space vector modulation (DSVM) and the optimal switching sequence (OSS) is proposed for achieving switching frequency (SF) and computational burden efficiencies in motor drives fed by two-level voltage source inverter. The DSVM is used to extend the prediction candidates of MPFC and greatly improve the performance of the controller. A generalized minimum flux error method independent of the number of virtual vectors is derived to cancel the exhaustive optimization method and lower the execution time of the proposed algorithm. In addition, new over-modulation and OSS schemes are designed to optimize the use of DC-link voltage and mitigate the inverter SF when implementing the optimal control action into switching states. The comparative experimental results show that without significant performance degradation, the proposed strategy provided about 50% SF and 25% execution time reductions compared to the classic MPFC methods.

Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

Low Complexity Zero-Suboptimal Model Predictive Torque Control for SPMSM Drives Based on Discrete Space Vector Modulation

Discrete space vector modulation and optimized switching sequence model predictive control for three-level voltage source inverters

This paper proposes a discrete space vector modulation and optimized switching sequence model predictive controller for three-level neutral-point-clamped inverters in grid-connected applications. The proposed strategy is based on cascaded model predictive control (MPC) for controlling the grid current while maintaining the capacitor voltage balanced without weighting factor. To enhance the closed-loop performance, the external MPC evaluates 19 basic and 138 virtual vectors (VV) of the proposed space vector method. The optimal control voltage is then selected using an extended deadbeat method to reduce the execution time of the proposed control algorithm. By using the discrete space vector modulation principle, the VV are synthesized based on switching sequence (SS) and are divided into negative and positive SSs considering their impact on the neutral point (NP) potential. The inner MPC evaluates both types of SSs and selects the one that keeps the capacitor voltage balanced. Various controllers are evaluated and compared against the proposed control strategy. The results show that the proposed strategy improves performance without weighting factor, while maintaining a total harmonic distortion of current to be less than 2%. Compared to the modulated MPC which provides the same fixed switching frequency, the proposed controller reduces the computational burden by over 50% while also providing better NP voltage balance accuracy.

DSVM-Based Model-Free Predictive Current Control of an Induction Motor

Classical model-free predictive current control (MFPCC) is a robust control technique for a two-level inverter-fed induction-motor drive, with advantages that consist of a simple concept, rapid response, simple implementation, and excellent performance. However, the classic finite-control-set MFPCC still exhibits a significant current ripple. This article presents a method to enhance performance using a combination of model-free predictive current control (MFPCC) and discrete-space vector modulation (DSVM). The MFPCC employs an ultralocal model with an extended-state observer (ESO) that does not consider motor parameters, therefore improving the control system’s reliability by eliminating the parameter dependency. The proposed method integrates DSVM, which divides a single sample period into N equal intervals and generates virtual vectors to reduce stator current ripple. It achieves the minimum cost-function value across the entire operating range of the induction-motor (IM) drive by selecting the optimal vector from a limited set of permissible voltage vectors. Using DSVM effectively reduces the total harmonic distortion (THD) without any detrimental effects during transients or steady states. Experimental studies validate the effectiveness and superiority of the suggested technique over the Finite-Control-Set (FCS) MFPCC, which only considers real voltage vectors in its computations.

Model predictive torque control for permanent magnet synchronous motor with hybrid mode duty cycle optimization

AbstractDue to the application of only one voltage vector in a control period, conventional model predictive torque control (MPTC) suffers from relatively large steady‐state ripples. The‐state‐of‐the‐art double‐vector‐based MPTC (DVMPTC) was proposed to improve steady‐state performance. However, its duty cycle optimization method significantly affects the control performances. Thus, MPTC with hybrid mode duty cycle optimization is proposed, which realizes better performance than the universal DVMPTC, and obtains the appropriate average switching frequency, especially at high‐speed domain. The proposed method utilises the universal DVMPTC method, while it equips duty cycle optimization with discrete space vector modulation (DSVM) scheme as the additional constraint for further reducing the reference voltage tracking error that exists in DVMPTC. What's more, rotation module and pre‐selection strategies are involved in simplifying the design of vector selection methods. Comparative simulations and experiments verify the effectiveness of the proposed method.

An Improved Model Predictive Torque Control for PMSM Drives Based on Discrete Space Vector Modulation

In this paper, an improved model predictive torque control (MPTC) method based on discrete space vector modulation (DSVM) is proposed for permanent magnet synchronous motor (PMSM) drives. Aiming at solving the two problems of large torque ripples and high computational complexity in conventional MPTC, the proposed method adopts a second optimization and a new simplified search strategy. The key idea of second optimization is to make the output voltage vector closer to the actual optimal solution. In this case, a more suitable voltage vector is applied in each sampling period. The simplified search strategy reduces the calculation time by cutting down the number of candidate voltage vectors without affecting drives performance. Compared to the conventional MPTC without DSVM and with DSVM, the proposed method can produce superior steady-state performance and lower computational complexity. Simulation and experimental results are presented to validate the effectiveness and feasibility of the proposed method.

Model predictive current control with modified discrete space vector modulation for three-leg two-phase VSI

Model predictive current control with modified discrete space vector modulation for three-leg two-phase VSI

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.

Finite Control Set Model Predictive Torque Control With Reduced Computation Burden for PMSM Based on Discrete Space Vector Modulation

This paper proposes a finite control set model predictive torque control (FCS-MPTC) with reduced computation burden for permanent magnet synchronous motor (PMSM). Based on discrete space vector modulation (DSVM), the proposed DSVM-MPTC extends the control set. Therefore, compared to the traditional FCS-MPTC, it greatly reduces the torque ripples and current harmonics while maintaining a constant switching frequency. Furthermore, a new voltage vector optimization strategy is employed to decrease the number of candidate voltage vectors from 38 to 13, the proposed DSVM-MPTC significantly reduces the computation burden without compromising the control performance. The validity of the proposed DSVM-MPTC is verified by comparative experiments.

A Unidirectional Voltage Vector Preselection Strategy for Optimizing Model Predictive Torque Control With Discrete Space Vector Modulation of IPMSM

Discrete space vector modulation (DSVM) is employed to synthesize large number of voltage vectors (VVs) to improve the steady-state performance of predictive torque control (PTC). However, enumerating all the VVs in the prediction process increases the computation load of the motor drive. To address this issue, this article proposes a unidirectional VV preselection (UVVP) method for optimizing the performance of DSVM-based PTC of ac motors. The proposed UVVP method divides both voltage space vector diagram (VSVD) and flux space trajectory into twelve 30° based sectors to reduce the impact of candidate VVs with negative effects. At each flux position, the UVVP strategy can restrict the nearest candidate VVs in the 30° based VSVD region within the circular flux trajectory. This is achieved by using the speed direction to avoid the candidate VVs, which are in the same flux sector for reverse flux rotation, hence resulting in significant flux and torque ripple reduction. The main benefit of the proposed method is its simplicity since it only requires the flux sector and speed information as well as it can generate the VV enumerations online while ensuring the reduction of computation burden.

Implementation of modified switching table for direct power control of shunt active power filter

ABSTRACT The direct power control (DPC) technique has been proposed as an alternative method to sophisticated current control techniques in order to synthesise the instantaneous active and reactive powers fed by the inverter into the grid. The method selects the voltage vector of the inverter based on the quantised values of the instantaneous active and reactive power errors and thereby controls the power exchange between inverter and the Point of Common Coupling (PCC) directly. The method has better dynamic performance, an inherent decoupling of active and reactive powers and robustness against parameter variations. However, the application of DPC to shunt active power filter (SAPF) requires high sampling frequency in the digital controller and at the same time suffers from the issue of the large variation in the switching frequency. This work proposes implementation of a modified switching table of DPC using multisector Discrete Space Vector Modulation(DSVM) concept for the SAPF to alleviate the problem of high sampling frequency requirement and variation in switching frequency and reduce burden on the controller. The performance of the method is evaluated in MATLAB Simulink® environment, verified with 2 kVA laboratory prototype under various load conditions and compared with constant switching frequency-predictive deadbeat DPC method.

Model Predictive Control for PMSM Based on Discrete Space Vector Modulation with RLS Parameter Identification

Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, the control performance of DSVM-MPC is influenced by the accuracy of motor parameters and the select speed of optimal voltage vector. In order to identify motor parameters accurately, model predictive control for PMSM based on discrete space vector modulation with recursive least squares (RLS) parameter identification is proposed in this paper. Additionally, a method to preselect candidate voltage vectors is proposed to select the optimal voltage vector more quickly. The simulation model of RLS-DSVM-MPC is established to simulate the influence of different parameters on PMSM performance. The simulation results show that model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification has a better control performance than that of without RLS parameter identification.

An Improved Finite Control Set Model Predictive Current Control for a Two-Phase Hybrid Stepper Motor Fed by a Three-Phase VSI

In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one null VVs by evaluating a simple cost function and then applies the optimal VV directly to the VSI. Though the implementation is simple, it features a large current ripple and total harmonic distortion (THD). The proposed improved FCS-MPCC builds an extended control set consisting of 37 VVs to replace the original control set with only seven VVs. The increase in the amount of VVs helps to regulate the current more accurately. In each control period, the improved FCS-MPCC takes advantage of deadbeat control to calculate a reference VV, and only the three VVs adjacent to the reference VV are predicted and evaluated, which decrease the computational workload significantly. Build waveform patterns for all VVs in the unbalanced circuit structure to modulate the optimal VV using discrete space vector modulation, which improves the current quality in reducing current ripple and THD. The comparative simulations and experimental results validate the effectiveness of the proposed method.

Low Complexity Finite-Control-Set MPC Based on Discrete Space Vector Modulation for T-Type Three-Phase Three-Level Converters

In this article, a low complexity finite-control-set model predictive control (FCS-MPC) based on the discrete space vector modulation (DSVM) is proposed for T-type three-phase three-level (3P-3L) converters. Different from the conventional FCS-MPC, 48 virtual voltage vectors (VVs) of the converter are constructed by real VVs based on the DSVM. Thus, the performance of 3P-3L converters is significantly improved and the peak amplitude of high-order harmonics concentrates at the sampling frequency. Furthermore, two-stage FCS-MPC based on virtual VVs is proposed to reduce the computation burden. Its first stage selects one of six virtual VVs that minimizes the current tracking error. Then, these candidate VVs located in the same sector as the optimal virtual VV selected in the first stage are evaluated in the second-stage optimization. Thus, the computational efficiency has been greatly improved. To verify the validity of the proposed control method and show its superiority over the conventional FCS-MPC, experimental results are presented.

Three-level inverter-fed model predictive torque control of a permanent magnet synchronous motor with discrete space vector modulation and simplified neutral point voltage balancing

Three-level inverter-fed model predictive torque control of a permanent magnet synchronous motor with discrete space vector modulation and simplified neutral point voltage balancing

Two‐stage DSVM‐based torque ripple minimisation technique for the predictive torque control of the induction motor

In this study, a two-stage technique is proposed for reducing torque ripple in the Predictive Torque Control (PTC) of the induction motor, based on the Discrete Space Vector Modulation (DSVM) strategy. In the first stage of this method, the voltage vectors are appropriately arranged through an offline procedure, such that the torque ripple value is decreased. On the other hand, the application time of the voltage vectors during the sampling period is calculated in the second stage, by conducting some online calculations, based on the adjacent vectors of the DSVM technique. This results in further torque ripple reduction. The proposed method can effectively reduce the torque ripple value at different operating points, especially at lower speeds. Finally, to validate the proposed method performance, the control algorithm has been experimentally implemented for an induction motor fed by 2L-VSI; and it is compared with two-vector PTC and deadbeat approaches.

A low‐complexity FS‐MPDPC with extended voltage set for grid‐connected converters

The conventional finite control set model predictive control (FS-MPC) for converter control is a well-studied area, but performance degradation due to the finite candidate vector set is still limiting its practical applications. Extending the voltage vector set using discrete space vector modulation has been proposed as a solution to overcome the limitations, but the brute-force search inherent to FS-MPC increases the computational complexity for a larger voltage set. This paper proposes a technique to alleviate the above issue by avoiding the brute-force search that is being executed in FS-MPC. The technique utilises the basics of direct-power-control theory to cut down the number of candidate voltage vectors applied in each cycle in the optimization problem. In this work, a design example having a voltage vector set of 37 elements is considered, and the proposed technique narrows down the search to eight optimal vectors. The proposed controller is specifically designed for active–reactive power control of a grid-connected converter that interlinks an energy storage system to the grid. The system is modelled in MATLAB Simulink environment and simulations are carried out to analyse the performance in all four active–reactive bidirectional power flow modes. Results validate the performance of the controller, both in steady-state and transient conditions. Further, the reduction in computational complexity due to the proposed algorithm is evaluated. It is observed that the number of computations was reduced approximately by 75% after applying the proposed algorithm for a system with a 37 voltage vector set.

Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems

This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

Fault-Tolerant Predictive Torque Control Design for Induction Motor Drives Based on Discrete Space Vector Modulation

Four-switch three-phase inverters (FSTPIs) are generally applied as fault-tolerant topologies or cost-effective topologies for induction motor (IM) drives. However, conventional predictive torque control (PTC) for FSTPI-fed IM suffers from poor steady-state performance and tedious weighting factor assignment. In this article, a PTC based on discrete space vector modulation (PTC-DSVM) for an FSTPI-fed IM drive is proposed. First, the relationship between the dc-link capacitor voltage offset (CVO) and the load current is derived, and a stator voltage compensation strategy is proposed to suppress the CVO. Meanwhile, a reference-voltage-vector-error-based cost function that combines the reference stator flux vector control, the stator voltage compensation strategy, and the deadbeat control is designed to fully eliminate the weighting factors in the cost function of the conventional PTC. In addition, a DSVM scheme is developed to increase the number of admissible voltage vectors and improve the steady-state performance of IM drives. A preselection algorithm based on the offset voltage vector and sector division is designed to reduce the number of candidate voltage vectors. Extensive simulations and experimental results with a 2.2-kW IM are demonstrated to validate the effectiveness of the proposed control strategy.

Model Predictive Torque Control for Dual Three-Phase PMSMs with Simplified Deadbeat Solution and Discrete Space-Vector Modulation

Unprecise voltage vectors applied in conventional model predictive control (MPC) would cause additional ripples in electromagnetic torque. To eliminate the problem, this article proposes an improved model predictive torque control (MPTC) based on deadbeat solution and discrete space-vector modulation (DSVM) for dual three-phase permanent magnet synchronous machines (PMSMs). First, deadbeat-direct torque and flux control (DB-DTFC) algorithm is applied and simplified, so that the computational burden can be reduced. Second, the virtual voltage vectors (VVVs) are adopted in dual three-phase voltage vector space to reduce voltage harmonics. Also, the DSVM is further proposed in this VVVs based vector space to generate more voltage vectors. After that, the simplified DB-DTFC and the DSVM scheme are artfully combined to select suitable voltage vector candidates for the MPTC, and the best one is then chosen to control the dual three-phase PMSMs. Finally, both simulation and experimental results are given to verify the effectiveness of the proposed method.