Improved Model Predictive Current Control 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.

On Fatigue Damage-Oriented Through-Life Optimization and Control of High-Power IGCT Converters in Wind Energy Systems

Integrated gate commutated thyristors (IGCTs) are critical components in high-voltage, high-current, and high-power conversion systems, particularly in offshore wind energy systems. However, the working environment of offshore wind energy conversion systems is extremely harsh. In this article, we propose an active damage control approach aiming at enhancing the reliability of the conversion system. By employing electro-thermal modeling for the equipment of the offshore wind energy conversion system, the junction temperature and fatigue damage of IGCT are simulated during the operation process. Using the improved model predictive current control (MPCC) method, active damage control effectively regulates the switching frequency of IGCT. IGCTs are symmetrically distributed on each leg of the converter, so the lifespan of the two IGCTs on each leg is also considered to be similar. This method balances the life of the IGCTs on the three legs of the converter and optimizes their utilization to the maximum extent. These measures effectively enhance the reliability of the conversion system and lower the operation and maintenance cost of high-power IGCT converters. The effectiveness of the proposed method is validated by co-simulation results by ANSYS and MATLAB/Simulink.

An Improved Model Predictive Current Control of Five-Phase PMSM Drive for Torque and Flux Ripple Alleviation

An Improved Model Predictive Current Control of Five-Phase PMSM Drive for Torque and Flux Ripple Alleviation

Model predictive and SoC balancing control of a CHB inverter in battery energy storage application using reward functions

SummaryThis article presents an improved model predictive current control algorithm combined with a novel state of charge (SoC) balancing approach for a three‐phase cascaded H‐bridge inverter in battery energy storage applications. Based on the model predictive control with elimination of redundant voltage vectors, neighboring vectors of the last voltage vectors are used to find the optimal ones in only one control cycle, which solves the problems of high computational burden and slow dynamic response. To overcome the unsuitability of the conventional cost function in SoC balancing, the reward functions that effectively balance the inter‐phase SoCs, inner‐phase SoCs, and inner‐phase dc voltages are presented respectively. Simulation and hardware‐in‐the‐loop‐based experimental results validate the effectiveness of the proposed strategy.

Research on an improved model predictive current control for direct‐drive wave energy converters with linear generators

AbstractThis paper deals with the current control strategy of a permanent magnet linear generator (PMLG) for a direct‐drive wave energy converter (DDWEC). As the mover is in a linear reciprocating motion, the reference of the controller of the generator is dynamic fluctuations with the mover movement under maximum power point tracking (MPPT) conditions. Therefore, the dynamic requirements of the controller become higher. To increase dynamic control performance, an improved model predictive current control (IMPCC) strategy is proposed here. Through a three‐step cascaded optimization algorithm, the target voltage vector is optimized from the three dimensions of the sector of the voltage vector, the angle of the voltage vector and the length of the voltage vector. The target voltage vector is output to the three‐phase rectifier by using SVPWM to complete the entire control process. Simulation and experimental results show that the proposed control strategy effectively reduces the current ripple and the vibration of PMLG and improves the stability of DDWEC.

Improved Model Predictive Current Control With Series Structure for PMSM Drives

In order to improve the control performance of conventional finite-control-set model predictive control (FCS-MPC) method and keep the balance between the steady-state performance and switching frequency of the system, this article proposes an improved MPC method based on the conventional MPC method. First, the reference voltage vector of the next control instant is predicted to determine the candidate voltage vectors of the next two control instants. Then, an optimal vector selection way is proposed, in which candidate voltage vectors selected by the reference voltage vector and designed cost function structure in series. Moreover, the selection process of optimal voltage vector from the candidate voltage vectors is analyzed. Finally, the comparison experiments between the conventional FCS-MPC method and the proposed MPC method are conducted. And experimental results show the validity of the proposed control strategy.

Improved Model Predictive Current Control of NPC Three-Level Converter Fed PMSM System for Neutral Point Potential Imbalance Suppression

For the neutral point clamped (NPC) three-level converter fed permanent magnet synchronous motor (PMSM) system, the performance of the conventional model predictive current control (MPCC) algorithm will be deteriorated if the amplitude of the neutral point potential (NPP) is large. Additionally, the adjustment process of the weighted coefficients of the conventional MPCC algorithm is complex because of numerous control terms in the cost function. To solve the above issues, an improved MPCC algorithm is proposed in this paper. Firstly, Newtonian iteration is used to transfer the stator current into stator voltage in the cost function. Then, the NPP term in the conventional cost function can be eliminated by introducing the partition control of the NP potential, which also eliminates the whole adjustment process of weighting coefficients. Finally, based on the amplitude of the NPP, the amplitude and phase angle of medium and small vectors are modified to improve the control performance of the torque and flux. Experimental results show that the fluctuation of the neutral point potential can be suppressed rapidly. Meanwhile, the performance of the torque, flux and current are also improved compared with the conventional MPCC.

Improved Model Predictive Current Control of the versatile Buck-Boost Converter for a Photovoltaic Application

The digital implementation of all the control loops of a versatile buck-boost (VBB) dc-dc converter used in a stand-alone photovoltaic application is proposed in this paper to improve existing digital-analog sliding-mode-based implementations. All three control loops: maximum power point tracking (MPPT), fast input voltage regulation, and inner high-bandwidth current control, have been programmed in the same digital signal controller (DSC). A Model Predictive Control (MPC) based algorithm has satisfactorily solved the challenge of implementing the nominal 100 kHz switching frequency current loop. The MPC cost function is distributed throughout the algorithm to achieve three specific goals: the tracking of the reference current (G1), a quasi-constant steady-state switching frequency (G2), and the assurance that the duration of an interval is larger than the time required to calculate it (G3). The third goal requires the current control to toggle between peak- and valley-modes depending on the operating point. The correct fulfillment of these control objectives on the proposed MPC-based algorithm has been validated through simulations and experimental tests performed on a purpose built-prototype.

Improved Model Predictive Current Control for Three-Phase Three-Level Converters With Neutral-Point Voltage Ripple and Common Mode Voltage Reduction

An improved two-stage model predictive current control (MPCC) for three-phase three-level converters in the paper, which can simultaneously achieve common-mode voltage (CMV) reduction, effective dc-link capacitor voltage control, and decreased computational burden, is proposed. Compared to the other current control strategies, it has the capability of handling multiple control objectives and possesses a simple structure. Furthermore, it greatly reduces the CMV since the peak CMVs of all the candidate voltage vectors are constrained to be less than one sixth of the dc-link voltage. In addition, an optimal two-stage voltage vector selection mechanism is employed in the proposed method. It helps to significantly reduce the computational complexity. Simulation and experimental results are provided to verify the effectiveness of the proposed method.

Improved Finite Control Set Model Predictive Current Control for Five-Phase VSIs

Model predictive current control (MPCC) is widely studied and applied in five-phase VSIs, and 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 constructed to improve steady state performance and simplify computation complexity. However, V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s in five-phase VSIs were initially constructed using one large and one medium voltage vectors, and all existing works employed this method, which goes against the common-mode voltage (CMV) reduction. To address this problem, two improved MPCC (IMPCC) methods are proposed in this article. Firstly, the V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> s are constructed in a new way using four adjacent large vectors, which enables the proposed IMPCC methods to reduce CMV and low-order harmonic currents inherently. Thus, there are no harmonic and CMV terms in the cost function. Secondly, the duty ratio optimization is introduced and dwell time of the optimal V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> is estimated to reduce current ripples. Then, in order to achieve superior steady state performance, two switching patterns are designed, namely the asymmetrical and symmetrical ones. The asymmetrical pattern aims to reduce the switching frequency while the symmetrical one focuses on harmonics suppression. Finally, experimental comparisons between the proposed IMPCC methods and conventional methods are presented. Experimental results have verified that the proposed schemes can mitigate CMV, suppress current harmonics and reduce computation complexity, simultaneously.

Improved Model Predictive Current Control of Single-Phase Five-Level PWM Rectifier

In this paper, a single-phase five-level rectifier with coupled inductors is studied. First, a discrete mathematical model of a single-phase five-level rectifier is created in two-phase(αβ). A traditional single vector finite control set model predictive control (FCS-MPC) algorithm is improved to overcome the problems of a varying switching frequency, the large amount of time needed for calculation, and the inaccurate setpoint of current loop tracking. Then, the objective function of the system is established, and a simple objective function is used instead of an iterative optimization of the traditional FCS-MPC algorithm. At the same time, to eliminate the delay error and to reduce harmonic distortion, deadbeat control technology is introduced. Finally, the simulation and experimental results show that the improved model predictive current control algorithm not only retains the fast response of traditional model predictive control, but also has the advantages of fixed switching frequency, small calculation time, and small current steady-state error.

An Improved Model Predictive Current Control for PMSM Drives Based on Current Track Circle

Model predictive current control (MPCC) is a high-performance control strategy for permanent-magnet synchronous motor (PMSM) drives, with the features of quick response and simple computation. However, the conventional MPCC results in high torque and current ripples. This article proposes an improved MPCC scheme for PMSM drives. In the proposed scheme, the back electromotive force is estimated from the previous stator voltage and current, and it is used to predict the stator current for the next period. To further improve the steady state and dynamic performance, the proposed MPCC selects the optimal voltage vector based on a current track circle instead of a cost function. Compared with the calculation of cost function, the prediction of the current track circle is simple and quick. The proposed MPCC is compared with conventional MPCC and a duty-circle based MPCC by simulation and experiment in the aspect of converter output voltage and sensitivity analysis. Results prove the superiority of the proposed MPCC and its effectiveness in reducing the torque and current ripples of PMSM drives.

Improved Model Predictive Current Control for SPMSM Drives Using Current Update Mechanism

Finite-set model predictive current control (FS-MPCC) has been employed for machine drives owing to the good dynamic performance. Sensitivity to parameter variation is one of the main barriers to its widespread application. To overcome this barrier, this article proposes an improved FS-MPCC for surfaced permanent magnet synchronous machines (SPMSMs). The contribution of this article is developing a novel current update mechanism, where the variation of resistance, rotor flux linkage, and inductance is considered, in which a modified coordinate system that contains a stationary and rotary axis frame is introduced. In this case, the predicted parameter can be obtained more accurately based on this mechanism, which can suppress the disturbances caused by model parameter mismatch. In addition, SPMSM parameter mismatch effect on FS-MPCC performance is analyzed in this article, which testifies that resistance mismatch influence on current prediction error can be neglected in practical system. The proposed FS-MPCC is validated and compared with different state-of-the-art methods. Improvement of the proposed control strategy is validated by means of experimental results on a 2-kW test rig.

Model Predictive Current Control for an Open-Winding PMSM System With a Common DC Bus in 3-D Space

Improved model predictive current control (MPCC) strategies in 3-D space are proposed in this article for an open-winding permanent-magnet synchronous motor (OW-PMSM) system with a common dc bus. In order to simply determine the optimal voltage vector (VV), a cost function related to VVs is established to replace that related to stator currents in conventional MPCC and the weighting factors are obtained by theoretical derivation. In order to reduce the calculation time of the optimal VV, a 3-D space system is established and the candidate VVs and the reference VV can be placed in the 3-D space. Then by means of the space geometric theory, the optimal VV can be selected faster and only four calculations are required in each sampling period. For better tracking of the reference VV, a VV-tracking (VVT) MPCC strategy is proposed and three VVs are selected according to the volt-second balance principle. Experimental results verify the effectiveness of the proposed strategies.

Improved Model Predictive Current Control for SPMSM Drives With Parameter Mismatch

Model predictive current control (MPCC) can predict future motor behavior according to a motor model. In practice, however, motor parameters will vary at run time, and the parameter mismatch disturbances caused by the variation in motor parameters will deteriorate the MPCC performance. To suppress the parameter mismatch disturbances effectively, this paper proposes a modified MPCC with a current variation update mechanism. In contrast with the traditional current prediction equation that contains crude model parameters, the modified current prediction equation contains only measured information, taking advantage of the proposed current variation update mechanism, which can update the modified prediction equation within each sampling period. A simulation established by MATLAB software indicates that the proposed method can effectively suppress the parameter mismatch disturbances. Experiments are carried out to verify the correctness of the proposed method.

Model predictive current control combined sliding mode speed control for PMSM drive system

This work deals with an improved model predictive current control (MPCC) combined sliding mode speed control (SMC+MPC) method to reduce the tracking error of speed and current for permanent magnet ...

Model Predictive Current Control for Fault-Tolerant Bidirectional Voltage Source Converter With Open Circuit Fault and Unbalanced Grid Voltage

To improve the fault-tolerant operation capability of bidirectional voltage source converter (BVSC), an improved model predictive current control (IMPCC) for fault-tolerant BVSC, is proposed with balanced DC-link capacitor voltage under unbalanced grid voltage. The proposed method can maintain continuous operation even if power device faults and unbalanced grid voltage faults occur together. A current predictive model of the fault-tolerant BVSC is established. By using grid voltages and 90° lagging signals in the $\alpha \beta $ stationary coordinate system, the reference current calculation method is designed for BVSC to eliminate power ripple under unbalanced grid voltage, which can avoid the complex positive and negative sequence extraction. DC-link split capacitor voltage balancing is achieved by the improved cost function. Based on the current predictive model and improved cost function, the optimal space voltage vectors are selected for fault-tolerant BVSC. Compared to the existing predictive methods, the proposed IMPCC can balance DC-link capacitor voltage and eliminate the power ripples for fault-tolerant BVSC under unbalanced grid with simple implementation. The experimental results validate the effectiveness of the proposed control scheme.

Model Predictive Current Control for PMSM Drives With Parameter Robustness Improvement

In order to solve the parameter dependence problem in model predictive control, an improved model predictive current control (MPCC) method based on the incremental model for surface-mounted permanent-magnet synchronous motor drives is proposed in this paper. First, the parameter sensitivity of a conventional MPCC method is analyzed, which indicates that the parameter mismatches would cause prediction current error and inaccurate delay compensation. Therefore, an incremental prediction model is introduced in this paper to eliminate the use of permanent magnetic flux linkage in a prediction model. Among the parameter of the incremental prediction model, only inductance mismatch contributes to the prediction error, since the influence of resistance mismatch on the control performance is very small. Therefore, in order to improve the antiparameter-disturbance capability of the MPCC method, an inductance disturbance controller, which includes the inductance disturbance observer and inductance extraction algorithm, is presented to update accurate inductance information for the whole control system in real time. Finally, simulation and experimental results both show that the proposed method can effectively eliminate the influence of the parameter mismatches on the control performance and reduce the parameter sensitivity of the MPCC method.

Predictive Control of Bidirectional Voltage Source Converter With Reduced Current Harmonics and Flexible Power Regulation Under Unbalanced Grid

This paper proposes a simplified and improved model predictive current control (MPCC) scheme for grid-connected bidirectional voltage source converter (GC-BVSC) when the unbalanced grid voltages occur. In conditions of unbalanced grid voltage, the current harmonic components of the GC-BVSC increase significantly, and twice grid-frequency ripple exist in both active power and reactive power, which influence the output power quality. To reduce harmonic currents and power fluctuations, an improved MPCC (IMPCC) method is proposed, and a two-step predictive model of GC-BVSC is put forward. The current compensation values are expressed by grid voltages and their quadrature signals that lag 90 electrical degrees in the αβ stationary coordinates system. The cost function reducing both the power ripple and current distortion is designed. Compared to conventional methods, phase-locked loop, pulse width modulation, and complex positive and negative sequence extraction of grid voltage are not required. In addition, IMPCC presents a better performance by reducing both grid-connected current harmonics and power ripple of GC-BVSC with flexible bidirectional power regulation capability. Simulation and experimental results on power electronics-professional (PE-PRO) platform verify the effectiveness of the proposed scheme under both single-phase and three-phase unbalanced grid voltages.

Improved Model Predictive Current Control Strategy-Based Rotor Flux for Linear Induction Machines

The model predictive current control strategy considers all participating voltage vectors to select the optimal voltage vector, which needs comparatively longer computational time to make the future current most close to its reference values. In order to decrease the computation time, first, the mathematic model of linear induction machines (LIM) is built in αβ coordinate instead of dq coordinate to avoid the complex coordinate transformation. Second, the predictive model of LIM is analyzed in detail to get the principle of selecting optimal voltage vector without the complex computation. Moreover, the number of participating voltage vectors increases from 8 to 18 and the novel voltage vector modulation is proposed to further improve the performance of LIM without increasing extra computational burden. Finally, the proposed strategy is verified by comprehensive simulations and experiments.