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Copyright of Electric Drive for Locomotives is the property of Electric Drive for Locomotives Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The cascaded H‐bridge (CHB) converter can achieve rich functions, such as active rectification, active power filtering, distributed energy storage, etc. The control of the CHB converter is a multiobjective optimization problem. Finite control set model predictive control (FCS‐MPC) is an effective method developed in recent years to solve this problem. However, because of the large amount of calculation, traditional FCS‐MPC cannot be directly used to control the multilevel CHB converter. It is also difficult to tune the weighting factors in traditional FCS‐MPC. To solve these problems, this paper proposes an improved sequential MPC (IS‐MPC) method for the CHB converter. The proposed method divides the control objectives of the CHB converter into different layers according to their priorities and the control degrees of freedom of each layer can be adjusted according to the system state. Simulation and hardware‐in‐the‐loop (HIL) implementation on a three‐phase 7‐level CHB converter shows that the proposed method can effectively control the ac current and submodule voltage of the CHB converter and reduce the switching loss. Moreover, it significantly reduced the calculation burden of traditional FCS‐MPC, and the use of the weighting factors is also avoided. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Solid-state transformers (SSTs) are expected to become one of the most powerful and adaptable devices that allow controllable voltage, power factor correction, fault isolation, compact size as compared to their low-frequency (50 Hz/60 Hz) counterparts. The high-frequency isolation of single-phase modified quasi-Z-source AC–AC converter (SPM-qZAC) employing bidirectional switches to create the single-stage SPM-qZAC-based SST is proposed in this paper. The proposed topology offers all the benefits of conventional impedance source topologies, including single-stage power conversion with a small footprint, buck–boost operation and retaining or reversing the phase angle. Moreover, the presented converter topology allows to share the same ground between input and output voltage, continuous input current, no input–output LC filters and performs AC–AC power conversion without the use of DC storage, making it suitable for AC voltage regulation. This study introduces two modulation schemes for SPM-qZAC-based SST. Finite control set model predictive control (FCS-MPC) which is a current control technique at variable switching frequency is employed owing to the capabilities of modern digital signal processing. Further, an adaptive hysteresis band-based delta sigma modulation (DSM) technique that offers the benefit of constant switching frequency (CSF) is proposed as an alternative voltage control-based modulation of the same topology. Various performance indices including steady-state response, total harmonic distortion of source current and dynamic response are assessed through simulation studies using MATLAB/Simulink software and real-time simulation environment using RT-Lab with OPAL-RT OP4510. It is observed that SPM-qZAC exhibits good performance when modulated using either modulation techniques; however, CSF-DSM technique offers an additional benefit of constant switching frequency. [ABSTRACT FROM AUTHOR]

In the finite control set model predictive control (FCS-MPC) strategy of the grid-tied inverter, the current ripple (CR) affects the selection of optimal voltage vectors, which leads to the increase of output current ripples. In order to solve this problem, this paper proposes a CR reduction method based on reference current compensation (RCC) for the FCS-MPC strategy of grid-tied inverters. Firstly, the influence of the CR on optimal voltage vector selection is analyzed. The conventional CR prediction method is improved, which uses inverter output voltage and grid voltage to calculate current ripples based on the space state equation. It makes up for the shortcomings that the conventional CR prediction method cannot predict in some switching states. The improved CR method is more suitable for the FCS-MPC strategy. In addition, the differences between the two cost functions are compared through visual analysis. It is found that the sensitivity of the square cost function to small errors is better than that of the absolute value function. Finally, the predicted CR is used to compensate the reference current. The compensated reference current is substituted into the square cost function to reduce the CR. The experimental results show that the proposed method reduces the CR by 47.3%. The total harmonic distortion (THD) of output current is reduced from 3.86% to 2.96%.

The Virtual Synchronous Generator (VSG) emulates the characteristics of a synchronous generator to provide inertia and damping for renewable energy systems. In the case of using the NPC three-level converter structure, traditional control methods require complex dual-loop control and internal PI parameter tuning. Furthermore, although fixed-parameter VSG control can provide inertia and damping when a significant power load is switched in an islanded microgrid, it cannot guarantee frequency regulation performance. To address these issues, this paper proposes an NPC three-level VSG parameter adaptive finite control set model predictive control strategy. This method eliminates the need for dual-loop control and PI parameter tuning. By incorporating angular velocity deviation and its rate of change into adaptive adjustment, a Tracking-Differentiator (TD) is designed to calculate the rate of change of angular velocity. This approach avoids frequent fluctuation of adaptive parameters during load power switching and improves the frequency stability of the microgrid. The effectiveness of the proposed strategy is validated through simulation and experimental verification.

A direct speed control strategy based on finite control set model predictive control (FCS–MPC) with a voltage smoother is presented herein. In the proposed concept, a finite set of smoothed voltage vectors characterized by an adjustable amplitude and a movable origin is introduced as voltage candidates in the FCS–MPC scheme. The controller predicts the future current and speed and outputs the optimal smoothed voltage using pulse-width modulation. Owing to this control scheme, an abrupt change in the output voltage, which causes a large current ripple, is avoided without additional computational costs. Simulation and experimental results obtained using a permanent magnet synchronous motor fed by a two-level three-phase inverter show that the proposed method effectively reduces the current ripple while achieving a high dynamic drive compared with the conventional FCS–MPC scheme. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) has been widely studied and applied in power converters and machine drives. It takes advantage of the nonlinear finite switching states of the power converters, and a discrete model is used to predict the behavior of the system. The main advantages of FCS-MPC lie in easy inclusion of nonlinear constraints, simple structure and intuitive concept. However, FCS-MPC applied in multiphase machine drives will face with the challenge of heavy computation cost. In this article, a computationally efficient FCS-MPC method is proposed for multiphase drives. The concept of virtual voltage vector is introduced to eliminate low-order harmonic currents in open-loop mode, resulting in simplified prediction model and cost function. To further simplify the enumerative optimization process, the redundant vectors are eliminated, and only five evaluations are required. By this way, the computation complexity is considerably reduced and is approximately irrespective of phase number. Thus, it can be easily generalized to multiphase drives. Moreover, the merit of simple structure is kept, which is the most important characteristic of FCS-MPC. The proposed method is compared with the existing FCS-MPC methods to illustrate its effectiveness. The experimental results have verified that the proposed method can reduce computation complexity, achieve superior steady-state and dynamic performance. [ABSTRACT FROM AUTHOR]

Finite control set (FCS) model predictive control (MPC) has been widely emerged for the double-subspace control problem of five-phase permanent magnet synchronous motor (PMSM) drives thanks to the principles of the virtual voltage vectors (V3s), which improve the steady-state performance with low computational burden. However, the V3s are designed offline to include one-subspace components, which is not considered as an optimized solution. Therefore, this article presents an improved double-vector-based FCS-MPC scheme for five-phase PMSM drives, considering the control objectives of the two subspaces. The proposed scheme remedies the limitations of the conventional V3s using online selection of the two vectors combination and duty ratio calculation. To keep low computational burden, ten possible double-vector combinations are examined during every sampling interval for each fundamental α–β sector, which is defined using the deadbeat calculations. Compared with the standard FCS-MPC and the traditional FCS-MPC with V3s schemes, the proposed double-vector-based FCS-MPC scheme can achieve improved tracking accuracy and high dc-link voltage utilization. The potentials of the proposed scheme over these existing FCS-MPC schemes are verified by simulation analysis and experimental test on a five-phase PMSM drive prototype. [ABSTRACT FROM AUTHOR]

In order to realize sensorless control of permanent magnet synchronous motor (PMSM) with high performance in low speed region, a novel rotor position observer scheme based on finite control set model predictive control (FCS-MPC) is presented in this paper. Firstly, the FCS-MPC is used to predict the current and drive the PMSM by selecting the optimal control quantity that minimizes the cost function. Next, an adaptive second-order generalized integrator (ASOGI) with adaptive center frequency adjustment was designed to replace the band-pass filter (BPF) in the rotor position observer. The ASOGI can calculate the high frequency value that can be used for position estimation by the controller switching frequency. The current ripple inherent in the FCS-MPC is considered as the response current obtained by the high frequency injection (HFI) method. The current ripple after ASOGI filtering is input to the phase-locked loop (PLL) for phase locking to obtain the estimated rotor position. In addition, adaptive linear (Adaline) neural networks are used to identify sensitive motor parameters online to avoid mismatch of model parameters, which causes degradation of control performance. Simulation experiments and hardware experiments show that this scheme is excellent in both static and dynamic conditions. [ABSTRACT FROM AUTHOR]

Predictive errors restrict high performance of finite control set model predictive control (FCS-MPC) in the cascaded H-bridge multilevel inverter.In order to solve this problem, the influence mechanisms of parameter mismatch, model discretization, and sampling error on voltage and current prediction errors are analyzed in this article. A hierarchical multifactorial error correction method is proposed. The known voltage prediction errors of the previous adjacent prediction periods are first fed back to calculate the voltage predictive error factors and self-correct the next voltage prediction errors. And then the current prediction errors are calibrated by the corrected voltage and the calculated current predictive error factor. This proposed method involves a simple structure and a good inhibitory ability on multifactorial prediction errors. Finally, experimental results in a three-phase five-level cascaded H-bridge multilevel inverter support the theoretical analysis and the proposed method. [ABSTRACT FROM AUTHOR]

Standard finite control set model predictive controllers (FCS–MPC) suffer from a deteriorated current quality when applied to multiphase drives. This problem has been recently mitigated with the concept of virtual voltage vectors. Its application ensures reduced current harmonics by limiting the x–y voltage injection. An intensive research effort has been done seeking for the best vector combination to provide an optimized performance in FCS–MPC. On one hand, multivector strategies including a null vector refine the effective voltage production with enhanced current quality but the dc-link utilization decreases. On the other hand, the exclusive use of large virtual vectors (LVVs) ensures a higher dc-link utilization at the price of a poorer current quality at low-voltage operation. This article first proposes an extended version of multivector strategies using a zero vector by including a quartet of voltage vectors. The usage of five switching states into the predictive approach (MV5–MPC) improves the current quality but still lacks a suitable utilization of the dc-link voltage. To overcome this limitation, the proposed MV5–MPC is combined with LVV–MPC to provide a hybrid solution that simultaneously achieves a higher current quality and a satisfactory dc-link utilization. Experimental results confirm the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

In this paper, a finite control set model predictive direct power control (FCS-MPDPC) method based on satisfactory optimization, for single-phase three-level PWM rectifier, is proposed to achieve global optimization that takes into account all its objectives and constraints. Satisfaction optimization aims to obtain the satisfactory solution after coordination of multiple goals, instead of the optimal solution of a single goal. By replacing “optimal” with “satisfaction”, more control degrees of freedom are acquired, so that low-priority auxiliary control objectives can participate in the optimization process. Meanwhile, in order to enhance the description accuracy of FCS-MPDPC method for the future trend of PWM rectifier, the prediction time domain is extended from the traditional single-step to multi-step, and the average switching frequency model is established to realize the low switching frequency control. The satisfactory optimized FCS-MPDPC method proposed in this paper is compared, through simulation and experimental results, with the standard FCS-MPDPC method, which verifies the effectiveness and superiority of the proposed algorithm.

Finite control set model predictive control (FCS-MPC) based on virtual voltage vectors (V3s) is widely studied and applied in five-phase permanent magnet synchronous motor (PMSM) drives. The synthesized V3s can inherently eliminate xy-axes voltages and reduce the number of candidate vectors, which can significantly reduce the computation burden. However, the dc-link utilization is reduced due to the usage of V3s. To address this issue, an FCS-MPC method with improved dc-link utilization is proposed in this article. First, only large vectors are selected to predict the system evolution. The large vector that minimizes cost function is selected, and rescaled using the duty ratio estimation. Then, in order to control the third harmonic currents, three adjacent large vectors are applied to replace the selected vector, and the duty ratios are reassigned. Additionally, zero vectors are replaced with two opposite large vectors to reduce common-mode voltage (CMV). Finally, the proposed FCS-MPC is compared with conventional methods in experiments. Experimental results have verified that the proposed method can improve the dc-link utilization, and keep the superiorities of low CMV and low computation burden. [ABSTRACT FROM AUTHOR]

Microgrids being an important entity in the distribution system, and to get their full advantages by incorporating maximum distributed generation, standalone hybrid renewable energy systems (HRESs), being environmentally-safe and economically-efficient, are considered as the promising solution to electrify remote areas where the grid power is not available. In this work, a techno-economic investigation with an optimal design of HRES is presented to fulfill the domestic electricity need for a residential area of the Sherani district in the Province of Baluchistan, Pakistan. Nine case studies based on PV/wind/diesel/battery are analyzed based on net present cost (NPC), cost of energy (COE), and emission to decide the feasible solution. HOMER tool is utilized to accomplish modeling and simulation for economic analysis and optimal sizing. Simulation results demonstrated that HRES with PV-wind-battery is the most viable option for the specified area, and the optimal sizing of components are also obtained with $ 28,620 NPC and $ 0.311/kWh COE which shows 81.65 % reduction in cost and 100 % preserving in toxic emission while fulfilling 100 % energy demand with 67.3 % of excess energy. Furthermore, MATLAB/Simulink modeling for the optimally designed system is built for technical analysis while its effectiveness is proved by keeping dc and ac buses voltage constant, safe operating range of battery state of charge (SOC) with active power balance between HRES components, as well as efficient ac voltage quality, regardless of generation disturbances and load fluctuations. The output signal has total harmonic distortion (THD) of 0.30 % as compared to 5.44 % with the conventional control scheme. The novelty lies in the sequential application of both HOMER and MATLAB simulations of the proposed HRES model and validation of the proposition for the studied area; by using and implementing model predictive control (MPC) of a reconfigurable inverter.

In order to realize sensorless control of permanent magnet synchronous motor (PMSM) with high performance in low speed region, a novel rotor position observer scheme based on finite control set model predictive control (FCS-MPC) is presented in this paper. Firstly, the FCS-MPC is used to predict the current and drive the PMSM by selecting the optimal control quantity that minimizes the cost function. Next, an adaptive second-order generalized integrator (ASOGI) with adaptive center frequency adjustment was designed to replace the band-pass filter (BPF) in the rotor position observer. The ASOGI can calculate the high frequency value that can be used for position estimation by the controller switching frequency. The current ripple inherent in the FCS-MPC is considered as the response current obtained by the high frequency injection (HFI) method. The current ripple after ASOGI filtering is input to the phase-locked loop (PLL) for phase locking to obtain the estimated rotor position. In addition, adaptive linear (Adaline) neural networks are used to identify sensitive motor parameters online to avoid mismatch of model parameters, which causes degradation of control performance. Simulation experiments and hardware experiments show that this scheme is excellent in both static and dynamic conditions.

In this paper, a novel and non-invasive stator inter-turn short circuit (ITSC) online detection method is presented for an induction machine (IM), driven by a two-level voltage source inverter (2L-VSI) via finite control set model predictive control (FCS-MPC). The main idea of the proposed method is to utilize the controller itself as an observer: under the presence of a fault, the distribution of inverter switching states significantly deviates from the original balanced case. Therefore, by inspecting the inverter switching vectors, which are the outcomes of the FCS-MPC routine's online optimization procedure, a stator fault can be detected efficiently. It is observed that both the zero-vector allocation over the complex plane and the allocation among the active vectors are influenced by the presence of a stator short-circuit fault. The proposed fault detection strategy introduces little to no extra burden for processor and memory. Experimental results verify the proposed method, and inter-turn short circuits of two and three turns are confidently detected and located for a 500 W, two-pole IM. [ABSTRACT FROM AUTHOR]

For the direct-current (dc) electrical power distribution system onboard more electric aircraft, the voltage quality of dc bus is of a great concern since there could be significant harmonics distortions when feeding different power electronics loads. This problem can be potentially addressed by introducing a dc filter to the point-of-load converters regulated by the finite control set model predictive control (FCS-MPC). To optimize this filter, genetic algorithm (GA) is utilized for searching the optimal design, which guarantees a low mass and low power losses. Different from the conventional filter design methods, the proposed method treats $LC$ as design variables, which need to be optimized while ensuring the output power quality. First, relations among variables, operation conditions, and constraints are built based on commercial data and circuit simulations. Then, the design and optimization are developed with these relations and a Pareto front is finally given by GA. After that, the best design is obtained by an index integrating two objectives. Lastly, the design approach is verified by an experiment where an FCS-MPC regulated converter was used as a particular example fed by three different LC filters. [ABSTRACT FROM AUTHOR]

Traditional arc-suppression devices have a weak effect on the arc-extinguishing result of earth fault because of increased active and harmonic components of fault current. To solve the problem, in this paper, a flexible arc-suppression device based on a three-phase cascaded H-bridge (CHB) converter with auxiliary sources is further developed; on this basis, an arc-suppression method based on the improved finite control set model predictive control is proposed. The proposed approach, which uses a combination of two voltage levels in a sampling period to reduce the steady-state current error, controls the CHB converter to inject compensation current into the distribution network. Taking into account the reduction of switching losses and balancing heat in each H-bridge cell, a novel method that causes the switching transitions to be distributed evenly among the H-bridge cells is proposed to select the optimal switching combination. The tracking capability of the improved control method is analyzed, and the parameters affecting the arc-suppression performance are obtained. The simulation and experimental results show that the proposed method achieves an excellent performance of suppressing fault current and extinguishing arc. Moreover, the balance of switching transitions is also achieved.

Recently, finite control set model predictive control (FCS-MPC) has been successfully applied in the grid-tied inverter with the LCL filter. However, to achieve active damping and grid synchronization, many sensors are required, increasing cost, and complexity. In addition, a considerable computational delay should be addressed when it is experimentally implemented, which may degrade the performance of the overall system. In order to reduce the number of sensors, eliminate the computational delay, and enhance the control reliability of the system, a novel FCS-MPC strategy with merely grid-injected current sensors is proposed, which contains four compositions: virtual flux observer, state observer, delay compensation, and FCS-MPC algorithm based on estimations. A 3-kW/3-phase/110-V experimental platform is established to validate that utilizing the proposed observations-based control method with only grid-injected current sensors is capable to obtain satisfactory performance of grid synchronization and high-quality grid-injected current both under balanced and unbalanced grid voltage condition. [ABSTRACT FROM AUTHOR]

A non-linear control structure for a Photovoltaic (PV), battery and supercapacitor based stand-alone DC-microgrid is presented in this paper. Most of the conventional PI-based linear control structures reported in the literature have been designed around a single operating point and also supercapacitor voltage has not been regulated. Though supercapacitor voltage is regulated in some of the works, a dedicated controller has been used which increases controller count and makes designing/tuning of controllers together for desired system performance complicated. However, due to self-discharge or frequent discharge during operation, supercapacitor voltage can reach the lowest value which affects controller performance and even makes the system unstable. So, a robust non-linear controller: Interconnection Damping Assessment-Passivity Based Controller (IDA-PBC) is implemented as an outer loop controller to regulate DC-link and supercapacitor voltages. Also, a Finite Control Set-Model Predictive Control (FCS-MPC) which is intuitive in nature is implemented as inner current controllers. The proposed control structure is digital-friendly and can address the limitations of linear PI-control structure such as (i) Controller conflicts in designing the multiple controllers, (ii) Small region of stability (iii) Supercapacitor voltage regulation. Finally, the proposed control structure is verified through simulation and experimental results. [ABSTRACT FROM AUTHOR]

Adding a Battery Energy Storage System (BESS) in the vicinity of renewable energy sources is a feasible solution to overcome the inherent negative impact caused by their random power fluctuating problems. Power conversion system performs as an interface between the battery packs and grid. The control method of power conversion system is essential to BESS. Finite Control Set Model Predictive Control (FCS‐MPC) is favorable to be chosen as the core control method for grid‐injected current regulation in such a system due to its outstanding benefactions, including fast dynamics, multiobjective optimization and simple implementations. Hence, this paper presents a FCS‐MPC with active damping feature for an Inductor(L) Capacitor(C) Inductor(L) (LCL)‐filter‐based power conversion system first. However, it is also well known that the practical effect of model predictive control significantly relies on the accuracy of mathematical model embedded within the controller. Parametric mismatch of the model tends to generate a prediction error, leading to a deterioration of the power quality of power conversion system or even instability issues furthermore. Therefore, this paper proposes an additional model correction strategy based on a comprehensive on‐line analysis on the difference between predictive and real value of respective voltage or current signals during the ever‐lasting several grid cycles to eliminate the influence of parametric mismatch. Simulation of an LCL‐filter‐based power conversion system is carried out to verify the validity of the theoretical analysis and control method in this paper. Finally, experimental results obtained from a down‐scaled prototype are provided to confirm the feasibility of the overall control strategy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive prediction horizon approach based on machine learning is presented for the finite control set model predictive control (FCS-MPC) of power converters. Usually, in FCS-MPC, the prediction horizon is kept constant. A large prediction horizon improves performance, however, it significantly increases the computational cost. The prediction horizon is typically chosen to be just large enough to give the required performance. We present a novel technique, where the prediction horizon adapts to the states of the converter. We define a cyber-physical objective function that penalizes both the error in converter performance and computational complexity. We perform several offline simulations to find the optimal prediction horizon based on the instantaneous state of the converter, based on a cyberphysical objective function. An artificial neural network is trained to calculate the optimal prediction horizon in run time. The proposed scheme allows a varying prediction horizon that reduces the overall computational complexity, while guaranteeing the required physical performance. The simulations and experimental results of the proposed technique justify the usefulness of our approach.

This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Faulttolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Measurement Science & Instrumentation is the property of Journal of Measurement Science & Instrumentation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new approach to performance validation of finite control set model predictive control (FCS-MPC) regulated power electronics converters is presented in this paper: statistical model checking (SMC). SMC is an established method used in various sectors of industry to provide solutions to problems that are beyond the abilities of classical formal techniques. The method is simple for implementation and requires only an operational system model that can be simulated and checked against properties. The approach will be presented on a standard two-level voltage source converter regulated by the FCS-MPC algorithm. In UPPAAL SMC toolbox, the converter system and controller are modeled as a network of stochastic timed automata. To assess the quality of the model, an equivalent Simulink model is used as a benchmark model. Using the model created in UPPAAL SMC toolbox the performance of the FCS-MPC algorithm is verified. The control algorithm is also tested on an experimental setup. During the evaluation, no significant degradation of reference tracking was found during transients nor under model parameter uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, new techniques are presented to improve the performance of the optimal switching vector model predictive control. A well-defined analytic technique is proposed for the real-time adjustment of dynamic weighting factors, as well as a new objective function formulation. These techniques provide a dynamic controller behavior, automatically adapted to distinct operating points. This ensures better steady-state performance in different conditions while improving simultaneously the dynamic response. The proposed controller presents a lower computational load than the conventional formulation and its design requires less extensive testing. General guidelines are presented to guide the user in the controller design. Simulation and experimental results are presented, demonstrating the advantages and superior performance of the proposed controller. [ABSTRACT FROM AUTHOR]

The single-phase matrix converter is an AC-AC power topology which consists of six bidirectional switches and it is considered the key unit in cascade or multilevel configurations. In this paper, a comparison between two control techniques is presented, one based on a proportional-integral-derivative control module with a pulse width modulator, and the other known as finite-state model predictive control. Simulation and experimental results are presented and discussed to demonstrate the feasibility and performance of both techniques.

In order to limit the armature current within a safe region, this paper proposes a finite control set model predictive control (FCS-MPC) method for linear induction machines (LIMs) by adding a penalty overcurrent term in the cost function. The modulation strategies based on two and three voltage vectors are combined with FCS-MPC so as to reduce the current ripples and steady-state tracking errors. Three different search methods have been developed to help reduce the computational burden by excluding many unsuitable participating voltage vectors and combinations in advance. These search methods are also effective for FCS-MPC with current limit. Finally, some key performance indexes are fully compared for LIMs controlled by different FCS-MPC-based modulation strategies through comprehensive numerical simulation and experiments on a test bench with two 3-kW arc induction motors. It is shown that the proposed low-complexity control strategies can effectively avoid overcurrent under any circumstances. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of deploying a finite control set model predictive control (FCS-MPC) algorithm for dynamic stabilization of a dc microgrid (MG) that supplies tightly regulated point-of-load (POL) converters. Within their control bandwidth, such converters behave as constant power loads (CPLs), where the MG sees them as impedances with a negative incremental resistance. Due to this characteristic, POL converters have a destabilizing impact that may cause large voltage oscillations or even a blackout of the whole MG. This paper proposes an active damping method realized by introducing a stabilization term in the cost function of the FCS-MPC algorithm that is used for regulation of the POL converter. This approach, on one hand, stabilizes a dc MG without implementing any additional active or passive components; thus, providing higher energy efficiency and better cost-effectiveness than methods that rely on such components. On the other hand, when compared to other approaches that focus on dc link stabilization via POL converter control, the proposed method has a significantly lower influence on the load voltage regulation performance. These findings are confirmed through comprehensive analytical investigation that shows how the proposed stabilization term affects the input impedance of the POL converter and the load voltage tracking performance. This is followed by experimental validation, where an FCS-MPC regulated uninterruptible power system inverter was used as a particular CPL example. [ABSTRACT FROM AUTHOR]

This paper presents two new implementations of finite control set model predictive control (FCS-MPC) methods applied to ac–dc converters with an inductive.capacitive.inductive (LCL) filter. The LCL filter, despite its advantages, can cause a strong resonance in the grid current and also pose a substantially more complex control problem. The new solutions improve the FCS-MPC with an active damping algorithm by eliminating low-order grid current harmonics and decreasing sensitivity to grid voltage distortion. The new methods, i.e., PCi1i2uc and PCi1i2uc-2steps propose multivariable approaches using converter-side current, line-side current, and capacitor voltage. Another improvement involves extending the prediction horizon in PCi1 i2uc-2steps. Both methods have been tested and compared in steady and transient states as well as under grid voltage disturbances. The methods were also compared with a predictive control algorithm with active damping. The simulation results and experimental measurements validate the developed control schemes and show high quality of grid current (low THDi value), high dynamic performance, and immunity under distorted grid voltage conditions. [ABSTRACT FROM AUTHOR]

The reliable operation of the power electronics system of an electric drive is a critical design target. Thermal cycling of the semiconductors in the power module is one of the main stressors. Active thermal control is a possibility to control the junction temperatures of power modules in order to reduce the thermal stress. In this paper, the finite control set model predictive control is designed for thermal stress based driving of electric drives converters. The optimal switching vector is selected using a multiparameter optimization that includes the current reference error, the additional thermal stress that a specific switching vector applies to each semiconductor, the temperature spread between semiconductors in the module, overall efficiency, and device constraints. This enables relieving the stress due to thermal cycles and reducing unsymmetrical fatigue of the modules chips while avoiding unnecessary losses. The approach is derived in theory and applied in simulation and experiment. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with merely grid-injected current sensors for an inductance-capacitance-inductance (LCL)-filtered grid-tied inverter, which can obtain a sinusoidal grid-injected current whether three-phase grid voltages are balanced or not. Compared with the conventional FCS-MPC method, four compositions are added in the proposed FCS-MPC algorithm, where the grid voltage observer (GVO) and Luenberger observer are combined together to achieve full status estimations (including grid voltage, capacitor voltage, inverter-side current, and grid-injected current), while the sequence extractor and the reference generator are applied to eliminate the double frequency ripples of the active or reactive power, or the negative sequence component (NSC) of the grid-injected current caused by the unbalanced grid voltage. Simulation model and experimental platform are established to verify the effectiveness of the proposed FCS-MPC strategy, with full status estimations under both balanced and unbalanced grid voltage conditions.

This paper proposes an error-voltage-based open-switch fault-diagnosis a strategy for a matrix converter (MC). A finite control set model predictive control method is used to operate the MC. The MC system performances under normal operation and under a single open-switch fault operation are analyzed. A fault-diagnosis strategy has also been implemented in two steps. First, the faulty phase is detected and identified based on a comparison of the reference and estimated output line-to-line voltages. Then, the faulty switch is located by considering the switching states of the faulty phase. The proposed fault-diagnosis method is able to locate the faulty switch accurately and quickly without additional voltage sensors. Simulation and experimental results are presented to demonstrate the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) has been accepted as an effective and feasible method in variable-speed drive applications. Considering the discrete nature of the converters and using a cost function for state selection reduce the computational burden despite of having a precise control. In this paper, FCS-MPC has been used to develop a novel method for induction motor control. Contrarily to conventional direct torque and flux control, in the proposed method, the torque and the angle between stator current and rotor flux are controlled. The fluxes are not directly controlled. This method increases the efficiency of the motor in light loads without using online optimum flux calculation. The angle control has become feasible by using FCS-MPC without imposing time-consuming calculations. Closed-loop prediction model is used to increase the precision of the angle prediction. A novel and simple gain calculation is also proposed. The proposed method has been evaluated by both simulations and experiments. [ABSTRACT FROM PUBLISHER]

This paper proposes the switching strategy based on finite control set model predictive control (FCS-MPC) method, to reduce switching losses and obtain balanced loss distribution of the voltage-source inverters (VSIs). Unlike the conventional FCS-MPC method with no explicit information of the reference voltage, the developed voltage-based FCS-MPC scheme produces the future reference voltage vector with the Lyapunov function every sampling period. With information of both the future reference voltage and the future load current vectors, the proposed switching strategy instantaneously determines one optimum clamped phase among the three legs in the VSI every sampling period. By optimally determining the clamping phase and its duration on the basis of every sampling period, the proposed switching strategy can successfully reduce the VSI switching losses. In addition, the proposed switching method can yield a balanced loss distribution among the switches in the VSI, contrary to the conventional FCS-MPC. The balanced loss generation as well as the switching loss reduction by the proposed method, which is optimal at the sampling period scale, is directly incorporated with the platform of the FCS-MPC algorithm, since the FCS-MPC operates on the basis of the sampling period. Thus, the proposed switching operation based on the voltage-based FCS-MPC algorithm enables the future VSI output currents to track the future reference current vector, as well as results in the reduced switching losses and the balanced loss performance. [ABSTRACT FROM AUTHOR]