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This work proposes the application of a modulated model predictive control (MMPC) to a neutral point clamped (NPC) converter, aiming to achieve a highly efficient and stable power conversion across a wide range of operating conditions. The dynamic nature of grid environments, particularly the potential for fluctuations in grid frequency, presents significant challenges for maintaining system stability and performance. This work implements a phase-locked loop (PLL) algorithm designed to effectively adapt to variations in grid frequency to address these challenges. The PLL algorithm was optimized to ensure that the system can maintain precise phase synchronization and consistent control performance. The integration of the MMPC with the PLL algorithm enhances the control strategy, improving the NPC converter's reliability and efficiency while also enabling it to effectively handle grid frequency variations. Experiments were conducted with a 1-kW NPC converter to validate the proposed controller. Simulation and experimental results confirmed the validity of the controller. [ABSTRACT FROM AUTHOR]

The modulated model predictive control (MMPC) method utilizes multiple voltage vectors in sequence within one switching cycle, achieving a fixed switching frequency. However, the control performance of using the MMPC technique still suffers from imprecise vector duration derivation and high computational burden. To address this, this paper proposes an improved MMPC technique aided by the Lagrange multiplier method to accurately calculate the duration of each vector, and then efficiently select the optimal voltage vector. Compared to the existing MMPC techniques, the proposed one exhibits a lower computational burden and superior steady‐state performance. Those benefits have been proven by the experimental results of a 1.5 k rpm permanent magnet synchronous motor drive. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Modulated Model Predictive Control (MMPC) techniques can be applied to enhance matrix converter (MC) performance in the linear modulation zone (LMZ). However, extending the converter operation in the overmodulation zone (OMZ) remains a problem. A key limitation of existing control methods in the OMZ is using the duty cycles calculated for the LMZ without considering the voltage or current reference vectors. This approach can lead to inaccurate calculations and a slower transient response when switching between the two operational zones. To overcome this challenge, a novel reference vector calculation method is proposed in this paper. The proposed approach simplifies the duty cycle calculation and ensures optimal performance in the OMZ by introducing new vector calculations and dividing the OMZ into two subzones. This method also minimizes the transition time between LMZ and OMZs to maximize the usable operating range of MCs. Simulation and experimental results validate the proposed method outperforms two other MMPC methods in improving the MC performance, reducing the transition times between operational zones, and maximizing the converter utilization.

ABSTRACT The unidirectional multilevel rectifier has immense potential in high‐power medium‐voltage industrial applications. This paper proposes a multiobjective control method based on the combination of optimal‐vector‐pair modulated model predictive control (OVP‐M2PC) and a phase‐shifted modulation strategy for a unidirectional five‐level rectifier. This strategy takes both of the inductor current tracking and capacitor voltage balancing as the control objectives. First, an optimal vector pair is determined based on the AC‐side current variation rate, and then, through combining the calculated action duration of OVP with carrier phase‐shifted modulation, inductor current tracking is achieved. Second, capacitor voltage balancing control is carried out by compensating for the duty cycles which also decouples the input current tracking and output capacitor voltage balance control. Compared with traditional FCS‐MPC, under the proposed control strategy, complex cost function calculation and weighting factor tuning are not required, which much reduced computational burden. Multiobjective control is achieved with better performance in both steady‐state and dynamic conditions. The superiority of the proposed strategy over traditional FCS‐MPC are validated through simulations and hardware experiments. [ABSTRACT FROM AUTHOR]

The multi-level inverter (MLI) is more efficient due to its ability to eliminate low-order harmonics. Thus, it is widely used in renewable energy generation, electric vehicles (EVs), etc. The choice of the neutral-point clamped (NPC) converter topology as a research object is of great significance in practical engineering. However, complex modulation algorithms and neutral-point voltage unbalance are two inherent problems for such NPC inverters. In this paper, an improved multi-voltage vector model predictive control (MVV-MPC) strategy based on neighboring four voltage vector synthesis is presented for three-phase NPC inverters. The duty cycle calculation and derivation methods for the proposed method are very simple, and they were obtained through the action time of each voltage vector being inversely proportional to the value of the corresponding cost function. Moreover, the fixed switching frequency of this strategy is maintained and the current peak ripple is reduced, guaranteeing the preferred total harmonic distortion of the output waveform. Furthermore, a voltage balancing method based on zero-sequence voltage injection is utilized to achieve dynamic balancing of the midpoint voltage difference and to limit the oscillation magnitude of the midpoint voltage difference to within ± 1 V. Finally, MATLAB simulations and an HIL experimental platform are built to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A large number of power electronic devices and nonlinear loads are connected to the coal mine power grid, causing a large amount of current harmonics and reactive power in the coal mine power grid. It seriously endangers the power quality of the coal mine power grid. The traditional coal mine power quality control strategies mostly use proportional integral (PI) regulators to control static synchronous compensator (STATCOM) to achieve harmonic suppression and reactive power compensation. But their parameters are difficult to adjust and their dynamic response is slow. In order to solve the above problems, a STATCOM control strategy based on modulated model predictive control (M2PC) is proposed. Firstly, the ip-iq method is used to detect the harmonic current and reactive current in the power grid as the reference current for M2PC. Secondly, based on the reference current and STATCOM mathematical model, the duty cycle and sector cost function of the two effective vectors and zero vectors for each sector is calculated. Thirdly, by minimizing the cost function, the optimal sector and the duty cycle of the two optimal effective vectors and zero vectors corresponding to that sector are obtained. Finally, according to the space vector modulation (SVM) method, switching pulses are allocated to achieve a fixed switching frequency. It thereby controls STATCOM to emit compensation current to offset harmonic and reactive currents in the power grid. The simulation and experimental results show that before the adopting of M2PC based STATCOM, the grid side current distortion is severe, the reactive power fluctuation on the grid side is large. The power factor on the grid side fluctuates and is less than 1. After the adopting of M2PC based STATCOM, the total harmonic distortion rate (THD) of the grid side current is significantly reduced due to STATCOM compensating for the harmonic current on the grid side. Moreover, due to STATCOM compensating for the required reactive power of the load, the reactive power on the grid side remains basically 0. The power factor on the grid side remains stable at 1, effectively improving power quality.

Summary: In this paper, a new floor function (F2) single‐carrier (SC)‐based modulated model predictive current control technique (M2PC2T) has been proposed by considering the conventional single‐phase five‐level packed U‐cell (PUC5) inverter topology. Generally, to regulate the output current, the finite control set‐based MPC technique without a modulator inherently operates with variable switching frequency, and it is unsuitable for industrial applications. To alleviate this, authors have implemented M2PC2T, which provides constant switching frequency of operation. In this technique, generally, the predictive algorithm has been integrated with any modulation stage. However, while the implementation of pulse width modulation (PWM) techniques, most authors have used conventional PWM techniques which further increases the control complexity as the level number increases. In this manuscript, a simple and generalized F2‐SC‐PWM technique has been proposed and integrated with the predictive algorithm. This PWM technique implementation process is very simple as compared with all conventional PWM techniques since even if the level number increases, the number of modulating and carrier signals is always only one. Finally, the complete closed‐loop control technique is called a generalized F2‐SC‐M2PC2T which is suitable for all stiff direct current (dc) sources and switched capacitor‐type multilevel inverter (MLI) topologies. The proposed control technique (PCT) has been experimentally verified with different transient and steady‐state case studies by considering resistive‐inductive load. [ABSTRACT FROM AUTHOR]

This paper presents a parallel single-phase to three-phase drive system, using two single-phase rectifiers. The rectifiers are not isolated from the grid by low-frequency transformers, so the reduction of the circulation current is an important objective for the control strategy. Modulated Model Predictive Control regulates the grid current and minimizes the circulation current between the parallel rectifiers. Furthermore, the analyzed predictive control consists of two strategies, where the first has the application of two vectors in a sampling period while the second has three vectors. The addition of a vector allows the reduction of the computational cost due to the reduction of tests performed in the predictive control and reduces the harmonic distortion in the electrical grid due to the greater application of vectors in a sampling period. Simulation and experimental results are presented in order to validate the control strategy.

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This research paper studies and highlights the features of the most popular finite control set model predictive control (FCS-MPC) strategies available in the state of the art, which are the optimal switching vector (OSV-MPC), modulated model predictive control (M2PC), and optimal switching sequence (OSS-MPC) methods. Thus, these strategies are studied experimentally by analyzing the transient and steady state performance using a grid tie conventional three-phase two-level voltage source inverter (VSI) with inductive output filter in a Typhoon HIL real-time simulator (RTS) with a Texas Instruments F28379D digital signal processor (DSP). Hence, quantitative indicators, such as the maximum tracking error, the mean absolute error, the settling time, the total harmonic distortion, the switching frequency spectrum, the switching pattern, and the computational burden are compared with the aim to deduce the best strategy for each criteria. [ABSTRACT FROM AUTHOR]

In the last decade, model predictive control (MPC) has been widely studied in power converters, such as voltage source inverters (VSIs). Unfortunately, MPC often presents a high computational burden that limits their applicability, especially when driving multilevel inverters (MLIs) because of their higher number of switching combinations than two-level inverters. As a result, some strategies have been developed to reduce the computational complexity of MPC. One of the most relevant is the use of artificial neural networks (ANNs) to approximate the behavior of an MPC. However, ANNs require to be evaluated at bounded inputs. Otherwise, their response cannot be guaranteed to be a good approximation of the controller they learned from. Furthermore, when driving an LC-filtered VSI, the inductor current can present high peaks due to the cross-coupling effect between the inductor and the capacitor. These current peaks can cause physical damages and loss of performance of an ANN-emulated MPC. This paper presents a new constrained modulated MPC (M2PC), better suited for ANN emulation, to overcome these issues. The proposed strategy evaluates the cost function once per switching region, allowing easy and intuitive constraint inclusion. Additionally, an overmodulation stage is used to handle negative duty cycles and enhance disturbance rejection. Finally, the proposal is validated through simulations, using MATLAB-Simulink, taking into account different load conditions. Simulations show that the constrained M2PC keeps the inductor current below its desired limit while having a good performance (low harmonic distortions and fast dynamics) even when the inverter operates near its boundaries.

Distributed energy resources are often connected to low-voltage distribution networks where the grid voltages may be unbalanced. This leads to an unwanted ripple in the output active power at twice the fundamental grid frequency. In this article, a new model predictive current controller is proposed for unbalanced grids. The variable switching frequency of existing model predictive controllers is fixed and the power quality is improved. A Kalman filter estimator is used to extract the positive and negative sequence components. A new calculation time compensation technique is proposed, which offers superior accuracy to existing approaches. A grid voltage discretization compensation strategy is outlined, and its effectiveness is demonstrated. Finally, the system stability is verified theoretically. Simulation and laboratory results are included to prove the robustness of the proposed controller and support the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a modulated model predictive control (M2PC) strategy for a nine-switch inverter (NSI) based electrification system to improve the steady-state performance. The model predictive control method has gained significant interest due to its straightforward structure. However, the traditional finite control set model predictive control (FCS-MPC) imposes a high computational burden that is problematic in practical applications. This prevents reaching the high sampling frequencies due to an excessive increase in algorithm run-time. Selecting a low sampling frequency causes an unpleasant distortion in the control variable or poor power quality. An M2PC method for the NSI is proposed in this work to remove this trade-off. One zero vector and two active vectors are selected by evaluating a cost function for each allowed switching state of the NSI. The duty cycles of these vectors are calculated by assessing the cost function employing current error terms. An optimized sequence of these vectors is applied to the system that operates with the fixed-modulation frequency. Thus, an improvement in power quality (reduced harmonics with a better spectral content) with a lower sampling frequency is achieved. The computational burden rate (CBR) on the processor is reduced. These enhancements were proved by simulation and experimental studies. The comparison work was conducted to highlight the advantages of the proposed method over the other techniques reported in the literature. The proposed M2PC method was verified on a lab-scale NSI prototype driving two induction machines. The machine torques and speeds are well regulated, and the quality of the stator current is improved. [ABSTRACT FROM AUTHOR]

This article presents a single-objective modulated model predictive control for a bidirectional dc–dc flying-capacitor (FC) converter in a microgrid. The presence of an FC facilitates the converter to integrate a low-voltage battery to a high-voltage dc bus at reduced voltage stress on its power switches. The converter in such a configuration demands a multiobjective controller to accomplish dc bus and FC voltage regulations and bidirectional power flow. The proposed controller realizes these multiple control objectives by determining the optimum duty ratio for the power switches using a single-objective cost function based on the battery current. In doing so, the converter realizes its multiple control objectives without weighting factors in the cost function and operates its power switches at a fixed switching frequency. The proposed controller also eliminates an additional control loop by utilizing an improved dynamic reference model to generate an appropriate battery current reference for the dc bus voltage regulation and bidirectional power flow. Finally, the proposed system is validated experimentally under step response of the dc bus voltage, load, PV power, and system parameter variations, and compared with a finite control set model predictive control to prove its effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a modulated model predictive control (MMPC) of a highly efficient and reliable inverter concept (HERIC) active front end (AFE) converter equipped with an LCL filter. The proposed method is based on the model predictive control (MPC) concept and obtains the optimized duty cycle by using three-level switching states which can be implemented in the HERIC converter. By doing so, the AFE converter achieves fast and precise current control. In addition, the obtained duty cycle is not limited to the polarity of the grid voltage, so it is possible to track the current reference more accurately near the zero-crossing points. The first-order approximation model of the LCL filter is derived to simplify the controller design process with the proposed MMPC. After that, the MMPC algorithm is adopted based on this simplified model in the single-phase system. The switching pattern of the HERIC topology is also proposed considering the MMPC under changing polarity of the duty cycle. To verify the performance of the proposed scheme, a 3 kW HERIC AFE converter was built and tested. Both simulation and experimental results demonstrate that the proposed scheme not only improves total harmonic distortion, but also achieves a fast-tracking performance. [ABSTRACT FROM AUTHOR]

Modular multilevel converter (MMC) is very popular in high power applications due to its attractive characteristics. The full bridge-based MMC featuring three-phase to single-phase direct ac/ac conversion is a potential solution for ac power supply (ACPS). In this paper, new modulated model predictive control (MMPC) methods are proposed for MMC-ACPS to improve the steady state multi-objective current tracking performance. In the proposed methods, modulated vectors sequence is employed, output voltage levels of upper and lower arms are combined and represented by vectors in the current increments plane first. Then, the plane is divided into eight sectors according to the predictive tracking errors of input current and circulating current. After determining sectors, a modulated vector sequence, consisting of one zero vector and two active vectors, is selected to eliminate these current tracking errors simultaneously at the end of each control period. Duty cycles of the three selected vectors are calculated based on the principle of multiple current tracking errors minimization. Optimized vector selection approaches are illustrated in detail for the proposed MMPC. Since only the adjacent nine vectors are utilized in the proposed methods, the calculation amount is suitable and the dv/dt of the output voltage is also limited. Finally, steady-state and dynamic performances of the proposed control methods are verified by experimental results. [ABSTRACT FROM AUTHOR]

Highlights • A Pareto-based Multi-Objective strategy is proposed for converter applications. • The use of weighting factors is not required for this multi-objective approach. • Soft and hard constraints can be implemented in this Pareto-based methodology. • The proposed methodology is validated using a 3 kW experimental system. Abstract Modulated model predictive control (M2PC) allows fixed switching frequency operation of power converters, producing lower ripple and lower total harmonic distortion (THD) in the output signals than those obtained using regular model predictive control (MPC) algorithms. However, the design of the M2PC algorithm and its performance depend on, among other factors, the tuning of weighting factors, which are required by the cost functions typically used in predictive control algorithms. In this paper, a novel Pareto-based multi-objective M2PC (MO-M2PC) strategy is proposed. In this case, the use of weighting factors is not required, and the optimization problem is solved using a multi-objective approach. The aim of the proposed MO-M2PC strategy is to consider practical rules, such as satisfaction of soft constraints, to calculate the control actions. The proposed Pareto-based MO-M2PC strategy can be applied to any power converter topology. In this paper, experimental validation of the proposed methodology is pursued using a 3-phase 3 kW power converter operating as a shunt active power filter (SAPF). The proposed control is implemented in a control platform based on the dSPACE ds1103 system. The simulation and experimental results demonstrate the advantages and flexibility achieved by the proposed Pareto-based MO-M2PC. [ABSTRACT FROM AUTHOR]

This paper proposes a new modulated model predictive control method for the control of modular multilevel converters (MMCs) in voltage source converter-high voltage dc systems. The proposed method retains the advantages of the conventional finite control set-model predictive control methods by programing the nonlinear properties of the MMC into the design calculations while minimizing the ac line and circulating current ripples and steady-state error by generating modulated switching signals with a fixed switching frequency. In this paper, first, the predictive modeling of the MMC is provided. Next, the proposed control method is described. Then, the application of the proposed method to an MMC system is detailed. Finally, experimental results from an MMC system connected to a three-phase grid are provided to validate the theoretical outcomes. [ABSTRACT FROM AUTHOR]