Voltage Vector Selection Research Articles

Model Predictive Current Control for Six-Phase PMSM with Steady-State Performance Improvement

The application of finite control set model predictive control (FCS-MPC) in six-phase permanent magnet synchronous motors (PMSMs) often faces a trade-off between computational burden and accurate voltage vector selection, as well as challenges related to harmonic components and torque generation. This paper introduces an improved model predictive current control (MPCC) method to address these problems. Firstly, 12 virtual voltage vectors are synthesized to improve torque output performance while suppressing harmonic currents. Then, to generate symmetrical switching signals and reduce switching loss, the largest basic vector used to synthesize the virtual vector is replaced by two medium vectors. Secondly, to solve the problem of the increased computational burden caused by the increase in discrete virtual vectors, a two-step vector selection method is proposed. In this method, each part is divided into several parts according to N, and the traditional cost function is also replaced by two-step functions. Different control performances can be achieved according to different values of N. Experimental results show that the proposed control scheme not only achieves stable current quality but also significantly improves steady-state performance throughout the entire speed range.

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.

A Simplified Model Predictive Current Control Strategy for Six-phase H-bridge Inverters

Background: The double-end H-bridge inverters can realize independent control of the stator voltage in each phase and have flexible advantages of modulation and fault tolerance; thus, they are more suitable for multi-phase fault-tolerant motor drives. However, due to the increase of voltage vectors and the coupling between the electromagnetic and non-electromagnetic quantities, the normal control strategies for traditional three-phase motor drives do not work anymore. Objective: This paper aimed to propose a simplified model predictive current control strategy based on the vector space decomposition (VSD) and the vectors’ gradual simplification for the three-level six-phase H-bridge inverters. Methods: Firstly, the 729 physical variables were decomposed and mapped onto the fundamental αβ subspace, harmonic xy subspace, and the zero-sequence o1o2 subspace based on the VSD. Then, to eliminate the influence of the harmonic and zero-sequence components on the control performance and make an easy digital implementation, vectors’ simplification has been proposed based on the in-depth analysis of the relationship between the voltage vectors mapped onto different subspaces and vectors’ stratification. With the simplification method, the number of voltage vectors was simplified from 729 to 12, and then the selected voltage vectors were used in the rolling optimization of the model predictive current control (MPCC) to choose the optimal one. Finally, sufficient experiments were carried out including static and dynamic conditions, different modulation index and power factor, etc., to verify the feasibility of the proposed strategy. Results: The simulation and experimental results show that with the simplified MPCC strategy, both the static and dynamic performances are relatively good, and the THDs of the phase current under different modulations and power factors are relatively low. Conclusion: The proposed MPCC algorithm for the three-level six-phase H-bridge inverters has shown obvious improvement in solving the control problems of multi-vectors and complex redundancy issues.

Parameter‐free predictive control with flexibility in power adjustment for grid‐connected converters under unbalanced grid conditions

AbstractThis paper introduces a novel finite control set predictive direct power control method for grid‐connected converters without cost function evaluations. Unlike conventional predictive direct power control, since the proposed method does not use the model parameters, their uncertainties do not cause prediction error and inappropriate voltage vector selection. The method employs a new form of voltage vector selection based on the slopes of active and reactive powers. The slopes are predicted in a manner with a low sensitivity to sampling noise, without updating a look‐up table, and recursive methods. Hence, there are no stagnation and convergence issues. Also, the proposed method avoids startup problems caused by data‐lacking due to directly regulating the active and reactive power by a switching logic. Flexible power oscillations control with balanced sinusoidal grid currents without any signal sequence extraction can also be achieved under this method in unbalanced grid conditions. The proposed method is assessed by both simulation and experimental studies, and its performance is compared with existing robust combined and model predictive control methods. The outcomes highlight the influence of the proposed approach and establish its superiority over the other considered methods.

An enhanced predictive current control technique for interior permanent magnet synchronous motor drives with extended voltage space vectors for electric vehicles

SummaryPermanent magnet synchronous motors (PMSM) are widely employed in the application of electric vehicles (EVs) due to their simplicity of operation. Model predictive current control (MPCC) is an advanced technique used to control the PMSM owing advantages like multi‐variable cost function and good dynamic performance. Cost function of predictive current control (PCC) does not require the flux weighting factor; hence, it is simple in the selection of suitable voltage vector (VV). The conventional PCC (C‐PCC) applied to interior PMSM (IPMSM) contains more torque and flux ripples as it includes only one set of voltage vectors for all speed ranges. In proposed PCC (P‐PCC), the magnitude and location of voltage vectors is to be selected as large and small VVs to reduce torque ripples. The P‐PCC in this article utilizes two set of extended voltage vectors (large and small VVs) based on the applied speed change in dynamic conditions and hence reduces the ripple content. Incorporating maximum torque per ampere (MTPA) control in this article serves the purpose of optimizing the machine performance. By adding MTPA to the proposed method, it is aimed to enhance the machine performance with less ripples and improved torque response. Simulation results are presented for conventional and P‐PCC to highlight the effectiveness and efficacy of the P‐PCC. The P‐PCC is experimentally verified with 3.7 kW PMSM using d‐SPACE controller.

Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss

The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM’s efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed.

Integration of predictive and computational intelligent techniques: A hybrid optimization mechanism for PMSM dynamics reinforcement

<abstract> <p>This paper presents an integrated approach combining a sequential neural network (SNN) with model predictive control (MPC) to enhance the performance of a permanent magnet synchronous motor (PMSM). We address the challenges of traditional control methods that struggle with the dynamics and nonlinear nature of PMSMs, offering a solution that leverages the predictive capabilities of MPC and the adaptive learning potential of neural networks. Our SNN-MPC model is contrasted with state-of-the-art genetic algorithm (GA) and ant colony optimization (ACO) methods through a comprehensive simulation analysis. This analysis critically examines the dynamic responses, including current, torque, and speed profiles, of the PMSM under proposed hybrid control strategies. The heart of the work deals with the optimal switching states and subsequent voltage injection to the inverter fed PMSM drive by a predefined minimization principle of a current modulated objective function, where MPC constitutes an integral finite control set (IFCS) mechanism for voltage vector selection and thereby selects the optimized integral gains <italic>K</italic><sub>d</sub> and <italic>K</italic><sub>q</sub> for direct and quadrature axes, respectively, with the FCS gain <italic>K</italic><sub>fcs </sub> obtained from implemented intelligent techniques. Based on the control criteria, the SNN-MPC scheme was established as the preferred benchmark with optimized tuning values of <italic>K</italic><sub>d</sub> = 0.01, <italic>K</italic><sub>q</sub> = 0.006, and <italic>K</italic><sub>fcs</sub> = 0.13, as compared to the gain values tuned from GA and ACO. The experimental setup utilized MATLAB and a Python environment for robust and flexible simulation, ensuring an equitable basis for comparison across all models.</p> </abstract>

A Computationally Efficient Model Predictive Control Method for Dual Three-Phase PMSM of Electric Vehicle With Fixed Switching Frequency

Aiming at the problems of large harmonic current, unfixed switching frequency and heavy calculation burden in the model predictive current control (MPCC) methods of dual three-phase permanent magnet synchronous motor (DT-PMSM). This paper presents a computationally efficient MPCC method with a fixed switching frequency. Firstly, the virtual voltage vectors (V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s) are synthesized by two large vectors and two medium-small vectors. The V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s effectively reduce the harmonic currents, and the goal of the synthetic V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is that the harmonic current in the x-y subspace is zero. Therefore, the weighting factor of the harmonic term is eliminated in the cost function. Secondly, an optimal voltage vector selection method is designed to reduce the candidate voltage vectors in the control set and relieves the calculation burden. In addition, to further reduce ripple current and torque ripple, the duty cycle modulation method is introduced to calculate the dwell time of the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The zero vector is inserted in the middle and both sides of the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s to achieve the purpose of fixing the switching frequency. Finally, the effectiveness of the proposed method is verified by experiments.

Improved Modulated Model Predictive Control Technique for PMSM Drives Using the Vector Projection

Compared to the model predictive control (MPC) technique, the modulated MPC (M2PC) method applies multiple voltage vectors in order within one switching cycle, enabling a fixed switching frequency and better motor control performance. However, the M2PC technique suffers from a significant computational burden and imprecise allocation method of the vector action time, affecting the control performance. To address these two issues, this paper proposes an improved M2PC method that utilizes the information of projection of the reference vector on the selected voltage vectors for efficiently selecting the optimal active vectors and also accurately calculating their action time. Compared to the conventional M2PC, the proposed technique demonstrates both excellent dynamic performance with reduced execution time by 15.3% and superior steady‐state performance in terms of lower torque/speed ripple as well as fewer current harmonics. Those benefits have been proven by the results of a 2.5k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

An improved model predictive control of back-to-back three-level NPC converters with virtual space vectors for high power PMSG-based wind energy conversion systems

In this study, an improved virtual space vector (VSV)-based two-stage model predictive control (MPC) scheme is presented for neutral point clamped (NPC) converters in high power-rated permanent magnet synchronous generator (PMSG)-based wind energy conversion system applications. The presented MPC scheme utilizes the VSVs and involves two-stage prediction for effectively reducing computation complexity. In stage-I prediction, a virtual space vector is identified based on the optimum cost function. Then, a set of sub-sector vectors are placed in stage II prediction based on the capacitor voltage levels. From this, the cost function predicts the optimum switching state. At the same time, the conditional selection of small voltage vectors in stage II prediction eliminates the neutral point voltage balancing constraint. Finally, the presented method is verified by the simulation of the back-to-back NPC converter of a high-power-rated PMSG-based wind energy conversion system. Further, the adaptability of the presented scheme for maximum power point tracking, reactive power control, and dc-link voltage control is also verified by simulation.

A Simplified Multivector-Based Model Predictive Current Control for PMSM With Enhanced Performance

To address the challenges of poor steady-state performance and large amount calculation of conventional model predictive current control (MPCC), a simplified multi-vector-based MPCC with enhanced performance is proposed in this paper. Firstly, an effective voltage vectors (VVs) selection method based on current-error is proposed, which can directly determine the optimal VVs without cost function enumeration. Compared with the standard method, the number of vectors that need to be evaluated is reduced from 7 to 1. Meanwhile, the duty cycle of each VV is calculated based on the current error to realize error-free control. In addition, to alleviate the deleterious effect of dead-time on the control performance, an improved MPCC scheme with optimal utilization of dead-time is proposed. The key is that the dead-time existing in MPCC is regarded as a dead-time voltage vector (DVV), which can be rationally utilized to improve the control performance. Both theoretical analysis and experimental results are given to verify the effectiveness of the proposed MPCC schemes.

A Low Switching Frequency Model Predictive Control Method for an Induction Motor Fed by a 3-Level Inverter

Traditional model predictive control (MPC) for the induction motor fed by a three-level inverter needs to explore 27 voltage vectors to obtain the optimal one, which leads to high switching frequency and requires too much computation. To solve this issue, a low switching frequency model predictive control with partition optimization is proposed. First, the reference voltage vector can be gained from the prediction model at the next time, and the space voltage vector plane is divided into 12 sectors for further vector choice. Furthermore, considering inverter constraints, the candidate voltage vectors are determined according to the sector location of the reference voltage vector. In this way, the candidate vectors can be reduced to 3 at most. Then, a boundary circle limit is designed to avoid unnecessary switch changes. If the reference voltage vector is within the boundary limit, the switches do not act, which can reduce the system switching frequency without introducing the extra weight coefficient into the cost function. These selected voltage vectors are substituted into the cost function to determine the optimal one. Finally, the neutral point voltage deviation is controlled by the positive and negative redundant small vectors to realize the multi-objective constraint without weighting coefficients. The simulation results show that the proposed control method can significantly reduce the switching frequency; at the same time, both the dynamic and steady performances can be maintained well, and the cost function has no weight coefficients.

Power-Compensated Triple-Vector Model Predictive Direct Power Control Strategy for Nonredundant Fault-Tolerant Rectifiers

A power compensated triple-vector model predictive direct power control (PCTV-MPDPC) strategy is proposed in this paper for non-redundant fault-tolerant three-phase rectifiers. The system fault tolerance ability and post-fault control performance are enhanced with low hardware complexity. By defining the negative conjugate of complex power as the control variable, a straight-forward pattern of power-voltage relationship analysis is obtained. Besides, a novel voltage vector selection method is put forward based on the location of the error vector between the reference and real control variables. The sector identification process can be omitted to derive a unified expression for duty ratio calculation. On top of that, an intuitive power compensation architecture is proposed to eliminate the dc-bus voltage deviation so that its utilization rate can be maximized. With the proposed PCTV-MPDPC strategy, the power ripples and input current harmonics can be significantly reduced. Superior steady-state and dynamic performance can be achieved compared with the conventional finite-control-set MPDPC (FCS-MPDPC) scheme and a recently proposed multiple-vector model predictive power control (MV-MPPC) method, along with excellent dc-bus voltage deviation suppression effect. Furthermore, simulation validation in Matlab/Simulink and experimental verification on a four-switch three-phase rectifier are carried out to demonstrate the effectiveness of the proposed PCTV-MPDPC strategy.

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.

The simulation analysis of stator flux droop minimization in direct torque control open-end winding induction machine

&lt;span lang="EN-US"&gt;Direct torque control (DTC) using dual-inverter technique is one of the best topologies for electric vehicle (EV) as it offers abundant selection of voltage vectors to drive the induction machine (IM). This dual-inverter technique also more reassuring as the system still workable even any of its voltage supply is disrupted or the power pack is drained. However, during the uneven voltage supply, the movement of voltage vectors is interrupted and will move obliquely especially in medium voltage vectors. This situation will lead to the faulty movement of the voltage vectors in the default sector definitions and lead to huge flux droop, which later could impose to distort phase current. This paper proposes an optimal sector definition based on the preset voltage ratio between the two inverters. The voltage vectors can be mapped tangentially to the flux vector, minimizing the flux droop and improving the phase current waveform when the proposed sector is utilized. The effectiveness of the proposed sector is tested using MATLAB/Simulink software and the exact parameter from the induction machine.&lt;/span&gt;

Common Mode Voltage Minimization for Generalized Dual Inverter-Fed Aircraft Starter/Generator: Realization and Sub-Load Asymmetry Influence Analysis

In multi-stages aircraft starter/generator system, the main machine (MM) and main exciter (ME) are fed by a generalized dual-inverter configuration, allowing for common-mode voltage (CMV) elimination. However, the distinction in load impedances and voltage requirements between the two sub-inverters has evidently contributed to the sub-load asymmetry, which may impair the suppression effect of system common mode (CM) noises. This paper presents a CMV minimization method for the generalized dual-inverter application. The available freedom, such as the selection and action sequence of voltage vectors, is appropriately arranged in the suggested implementation to eliminate the total CMV, restrict the sub-CMVs and fulfill the voltage requirements of sub-inverters all at the same time. In the meanwhile, a theoretical study is performed to evaluate the CM noise features of zero CMV technique in the generalized dual inverter-fed system. In the situation of sub-load asymmetry, the premise and main influence element for CM noise reduction are assessed. Finally, these findings are verified by simulation and experiments.

Predictive Stator Current Control of a Five-Phase Motor Using a Hybrid Control Set

Finite state model predictive control (FSMPC) of multiphase drives can use an extra number of inverter configurations compared with the three-phase case. This, however, requires more computing power for the optimization phase. The application time of each selected voltage vector (VV) is then increased, which can result in higher harmonic content. Reducing the allowed VVs can speed up the computations, thus ameliorating the current tracking/regulation in different orthogonal subspaces. However, the flexibility offered by the reduced set of VVs is less than that of the full set. Furthermore, a lower sampling time can result in an increase in the switching frequency, especially for some speed-load combinations. This article proposes the use of a hybrid scheme where the set of allowed VVs is not fixed but rather selected on-line according to the actual speed and torque producing stator current which are computed by the outer loop. A five-phase induction machine (IM) is used as a test bed for the proposal, showing improved results with respect to the nonhybrid case.

Improved Model Predictive Current Control for Linear Vernier Permanent-Magnet Motor With Efficient Voltage Vectors Selection

In order to relieve the computational burden and reduce the ripples in thrust force and current of linear vernier permanent-magnet motors, this article proposes three-vector-based model predictive current control (MPCC) with efficient voltage vectors selection. The proposed method avoids the traversal of all the possible voltage vectors. Only three nonadjacent active voltage vectors are predicted and evaluated by the cost function. Two active voltage vectors can be precisely determined according to the relationship of three cost function values. The prediction workload can be reduced with the efficient voltage vectors selection. Also, the two active voltage vectors along with the null voltage vector are used to synthesize the final applied voltage vector based on deadbeat principle. The proposed three-vector-based MPCC method extends the modulation area from discrete points to the whole regular hexagon. Thus, the ripples in thrust force and current can be minimized. Theoretical analyses and experimental results are given to verify the effectiveness of the proposed MPCC.

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.

Low-Complexity Dual-Vector Model Predictive Control for Single-Phase Nine-Level ANPC-Based Converter

This article proposes a dual-vector finite-control-set model predictive control (FCS-MPC) with reduced complexity for a novel nine-level active neutral point clamped (ANPC) converter. This topology considerably reduces the used number of power switches compared to other topologies. Only nine power switches and two flying capacitors (FCs) are used to generate nine voltage levels. The proposed MPC scheme notably reduces the computational burden by directly locating the best two vectors without the need for multiple evaluations of the cost function as in the conventional method. Using one weighting factor in the cost function, three objectives are considered, namely, current tracking, FCs voltage control, and dc-link stabilization, reducing the heavy effort of coordinating weighting factors. Mathematical analyzes were carried out to determine the optimal duration of the selected voltage vectors. While the sequence of the two voltage vectors is identified based on the total harmonic distortion (THD) definition to minimize its value. Compared with standard FCS-MPC, lower steady-state errors, lower THDs, better harmonic distribution, and shorter execution times are achieved. The proposed MPC method is validated and compared with other prior-art control methods through experimental implementation.