Search Results (53)

Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance. Full article

Grid-forming (GFM) inverters have recently seen wider adoption in microgrids and inverter-based-resource (IBR)-penetrated grids, and are primarily used to establish grid voltage under a wide array of conditions. In the existing literature, GFM control is almost exclusively applied using voltage-source inverters (VSIs). However, due to the inherent limitations of available semiconductor devices’ current ratings, inverter-side current must be limited in VSIs, particularly during grid-fault conditions. These limitations complicate the real-world application of GFM functionality in VSIs, and complex control methodologies and tuning parameters are required as a result. In the following study, GFM control is instead applied to a buck-type current-source inverter (CSI) using a combination of linear droop-control and finite-control-set (FCS) mode-predictive control (MPC) that will be referred to herein as hybrid model-predictive control (HMPC). The resulting inverter features a simple topology, inherent current limiting capabilities, and a relatively simple and intuitive control structure. Verification was performed on a 1MVA/630V system via MATLAB/Simulink, and the simulation results demonstrate strong performance in voltage establishment, power regulation, and low-voltage ride through under-grid-fault conditions, highlighting its potential as a competent alternative to VSIs in GFM applications, and lacking the inherent limitations and/or complexity of existing GFM control methodologies. Full article

The increasing integration of renewable energy, electric vehicles, and industrial applications demands efficient power converter control strategies that reduce switching losses while maintaining high waveform quality. This paper presents a Finite-Control-Set Model Predictive Control (FCS-MPC) strategy for three-phase, two-level voltage source inverters (VSIs), incorporating a secondary objective for switching frequency minimization. Unlike conventional MPC approaches, the proposed method optimally balances control performance and efficiency trade-offs by adjusting the weighting factor (${\lambda }_{min}$). Real-time implementation using the OPAL-RT platform validates the effectiveness of the approach under both linear and non-linear load conditions. Results demonstrate a significant reduction in switching losses, accompanied by improved waveform tracking; however, trade-offs in distortion are observed under non-linear load scenarios. These findings provide insights into the practical implementation of real-time predictive control strategies for high-performance power converters. Full article

In this study, we propose a long prediction horizon finite control set model predictive control (FCS-MPC) framework for PMSMs. Initial simulations using a one-norm cost function resulted in instability in switching frequency control, particularly due to the inherent limitations imposed by the sampling interval when no control effort was applied. To mitigate this, we reformulated the MPC framework using a two-norm cost function within a sphere decoding algorithm (SDA), which, at high sampling intervals (>40 $\mathsf{\mu }$s), resulted in an undershoot in the direct-quadrature axis. Extensive simulations were conducted over a range of sampling intervals (1–$80\mathsf{\mu }$s), revealing that while a $10\mathsf{\mu }$s interval achieved the lowest THD, it also led to an increased switching frequency. To address this trade-off, a weighting factor tuning approach was employed, effectively reducing switching frequency while maintaining acceptable THD levels. Further investigations analyzed the effects of three-step and five-step prediction horizons, as well as parameter mismatches in the long prediction formulation, providing critical insights into controller robustness. These findings underscore the importance of norm selection, sampling interval optimization, and weighting factor adjustments in balancing THD reduction and switching frequency. The proposed approach enhances system efficiency, reliability, and overall performance, offering significant implications for high-performance aerospace PMSM applications. Full article

In recent years, the study of model predictive control (MPC) in power electronics has gained significant attention due to its ability to optimize system performance and improve the dynamic control of complex power converters. There are two types of MPC: finite control set (FCS) and continuous control set (CCS). The FCS–MPC has been studied more in regard to these two types of control due to its easy and intuitive implementation. However, FCS–MPC has some drawbacks, such as the exponential growth of the computational burden as the prediction horizon increases and, in some cases, a variable frequency. In contrast, generalized predictive control (GPC), part of CCS–MPC, offers significant advantages. It enables the use of a longer prediction horizon without increasing the computational burden in regard to its implementation, which has practical implications for the efficiency and performance of power converters. This paper presents the design of GPC applied to single-phase multilevel voltage source inverters, highlighting its advantages over FCS–MPC. The controller is optimized offline, significantly reducing the computational cost of implementation. Moreover, the controller is tested in regard to R, RL, and nonlinear loads. Finally, the validation results using a medium-performance controller and a Hardware-in-the-Loop device highlight the improved behavior of the proposed GPC, maintaining a harmonic distortion of less than 1.2% for R and RL loads. Full article

This paper evaluates the performance of strategies based on finite-control-set model predictive control (FCS-MPC) aimed at reducing or fixing the converter switching frequency or decreasing the spread of the harmonic spectrum in multilevel hybrid cascade converters (HCCs). These properties are desirable for medium- to high-voltage applications, where minimizing switching losses is crucial, as well as for applications employing passive filters, where resonance modes can be excited. The strategies evaluated are input restriction, notch filtering, period control, and PWM restriction. Key aspects considered in this work are (i) the evaluation of the steady-state and transient performance of FCS-MPC strategies proposed for two-level converters in a multilevel topology, and (ii) the evaluation of the computational cost associated with the implementation of these strategies on a multilevel converter with a high number of available inputs. As a typical application, the study is carried out employing a five-level HCC experimental prototype driving an induction motor through indirect vector control. To perform a fair comparison between the strategies, a control platform based on a cost-effective Zynq system on chip is proposed, which allows for achieving the hard timing constraints imposed by FCS-MPC strategies. The results show that the PWM restriction strategy achieves the best steady-state performance among the evaluated strategies, with an error 400 times smaller than that of the second-best strategy (input restriction), with an average switching frequency of 962.5 Hz, which differs from the desired average frequency by 3%, and a maximum difference in power distribution between modules of 0.8%. In addition, the system-on-chip hardware achieves a competitive execution time of 46 μs when the ARM Cortex solution is implemented and 20 μs when the ARM Cortex–FPGA solution is used instead, employing the 512 inputs available in the FCS-MPC algorithm. The studies, performed in steady-state and transient regimes, confirm (i) the feasibility of the evaluated algorithms in an HCC topology and (ii) the feasibility of the control platform for implementing high-computational-burden algorithms with a low sampling time. Full article

Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Generator (DFIG), FCS-MPC has thus far exhibited promising results when compared to the conventional Proportional Integral control strategy. Recently, there has been research conducted regarding the reduction in switching frequency of FCS-MPC. Preliminary studies indicate that a reduction in switching frequency will result in larger current ripples and a greater total harmonic distortion (THD). However, research in this area is limited. The aim of this study is two-fold. Firstly, an indication into the effect of weighting factor magnitude on current ripple is provided. Thereafter, the research work provides insight into the effect of such weighting factor on the overall current ripple of FCS-MPC applied to the DFIG and attempts to determine an optimal weighting factor which will simultaneously reduce the switching frequency and keep the current ripple within acceptable limits. To tune the relevant weighting factor, the utilization of swam intelligence is deployed. Three swarm intelligence techniques, particle swarm optimization, the African Vulture Optimization Algorithm, and the Gorilla Troops Optimizer (GTO), are applied to achieve the optimal weighting factor. When applied to a 2 MW DFIG, the results indicated that owing to their strong exploitation capability, these algorithms were able to successfully reduce the switching frequency. The GTO exhibited the overall best results, boasting steady-state errors of 0.03% and 0.02% for the rotor direct and quadrature currents whilst reducing the switching frequency by up to 0.7%. However, as expected, there was a minor increase in the current ripple. A robustness test indicated that the use of metaheuristics still produces superior results in the face of changing operating conditions. The results instill confidence in FCS-MPC as the control strategy of choice, as wind energy conversion systems continue to penetrate the energy sector. Full article

Finite Control Set Model Predictive Control emerges as a promising method for controlling power electronics inverters, outperforming traditional linear techniques. However, implementing Finite Control Set Model Predictive Control on conventional processors faces a significant computational burden due to its repetitive nature. This paper presents a novel approach that utilizes LabVIEW & Field Programmable Gate Arrays to address this computational bottleneck. By capitalizing on the inherent parallelism and suitability of Field Programmable Gate Arrays for discrete control problems, substantial computational advantages are achieved for Finite Control Set Model Predictive Control. The use of LabVIEW, a well-established platform in industrial and commercial solutions, ensures that this work is relevant not only academically but also for real-world industrial applications of FCS-MPC in power electronics and motor drives. This research successfully demonstrates the application of Finite Control Set Model Predictive Control for controlling the current of a motor-like load for a three-phase Voltage Source Inverter system in LabVIEW. To simplify the traditionally complex Field Programmable Gate Arrays programming process, user-friendly toolkits such as LabVIEW Control Design & Simulation, LabVIEW Real-Time, and LabVIEW FPGA Module are employed. This LabVIEW-based integration facilitates the execution of both concurrent and sequential Field Programmable Gate Arrays algorithms, leading to efficient Field Programmable Gate Arrays resource management and user-defined restrictions on maximum switching frequency, obviating the need for resource-intensive control methods for fast switches such as SiC and GaN IGBTs. The proposed controller is validated using an off-the-shelf computer turned into a real-time system but also on Field Programmable Gate Arrays for comparison purposes. Full article

Model Predictive Control (MPC) has emerged as a promising alternative for controlling power converters, offering benefits such as flexibility, simplicity, and rapid control response, particularly when short-horizon algorithms are employed. This paper introduces a system using a short-horizon Finite Control Set MPC (FCS-MPC) strategy to specifically address the challenge of non-minimum phase behavior in boost converters. The non-minimum phase issue, which complicates the control process by introducing an initial inverse response, is effectively mitigated by the proposed method. A Proportional–Integral (PI) controller is integrated to dynamically adjust the reference current based on the output voltage error, thereby enhancing overall system stability and performance. Unlike conventional PI-MPC methods, where the PI controller has an influence on the system dynamics, the PI controller in this approach is solely used for tuning the reference current needed for the FCS-MPC controller. The PI controller addresses small deviations in output voltage, primarily due to model prediction inaccuracies, ensuring steady-state accuracy, while the FCS-MPC handles fast dynamic responses to adapt the controller’s behavior based on load conditions. This dual control strategy effectively balances the need for precise voltage regulation and rapid adaptation to varying load conditions. The proposed method’s effectiveness is validated through a multi-stage simulation test, demonstrating significant improvements in response time and stability compared to traditional control methods. Hardware-in-the-loop testing further confirms the system’s robustness and potential for real-time applications in power electronics. Full article

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. Full article

The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy. Full article

In islanded operation, precise power sharing is an immensely critical challenge when there are different line impedance values among the different-rated inverters connected to the same electrical network. Issues in power sharing and voltage compensation at the point of common coupling, as well as the reverse circulating current between inverters, are problems in existing control strategies for parallel-connected inverters if mismatched line impedances are not addressed. Therefore, this study aims to develop an improved decentralized controller for good power sharing with voltage compensation using the predictive control scheme and circulating current minimization between the inverters’ current flow. The controller was developed based on adaptive virtual impedance (AVI) control, combined with finite control set–model predictive control (FCS-MPC). The AVI was used for the generation of reference voltage, which responded to the parameters from the virtual impedance loop control to be the input to the FCS-MPC for a faster tracking response and to have minimum tracking error for better pulse-width modulation generation in the space-vector form. As a result, the circulating current was maintained at below 5% and the inverters were able to share an equal power based on the load required. At the end, the performance of the AVI-based control scheme was compared with those of the conventional and static-virtual-impedance-based methods, which have also been tested in simulation using MATLAB/Simulink software 2021a version. The comparison results show that the AVI FCS MPC give 5% error compared to SVI at 10% and conventional PI at 20%, in which AVI is able to minimize the circulating current when mismatch impedance is applied to the DGs. Full article

In this article, a finite control set-model predictive control (FCS-MPC) with variable sampling time is proposed. A zero-voltage vector appears in the dead time between specific voltage vectors, resulting in an unintentionally large common-mode voltage. Herein, a large common-mode voltage was suppressed, and the load current was controlled using a voltage vector combination that did not cause a zero-voltage vector in dead time. Additionally, to improve the total harmonic distortion (THD) of the load current, the intersection of the predicted current and the command current by all the volage vectors (VVs) in the combination is confirmed. The VV where the intersection occurs is selected as the optimal VV. This optimal VV is applied to the point where the predicted current and the reference current intersect. The applicable range of the sampling time should be selected by considering the calculation time and number of switching. Through the proposed FCS-MPC strategy, not only can the common-mode voltage be limited to within ±Vdc/6, but an improved THD can also be obtained compared to the existing method using fixed sampling. The proposed method was verified through PSIM simulation and experimental results. Full article

A diverse group of so-called multi-vector techniques has recently appeared to enhance the control performance of multiphase drives when a direct control strategy is implemented. With different numbers of switching states and approaches for estimating the application times, each multi-vector solution has its own nature and merits. Previous studies have individually tested each version of the proposed finite-control-set model predictive control (FCS-MPC) strategies using a single experimental setup with specific parameters and, in some cases, using a limited range of operating conditions and focusing exclusively on some control aspects. Although such works provide partial contributions, the control performance is highly affected by the test and rig conditions, being dependent on the machine parameters, the switching frequency and the range of operation. Consequently, it becomes difficult to extract some universal conclusions that guide the control designer on the best alternative for each application. Aiming to enrich the knowledge in this field and provide a broader picture, this work performs a global analysis with different multi-vector techniques, various machine parameters, multiple operating points and a complete set of indices. Experimental results confirm that the selection of the most adequate control strategy is not a trivial task because the degree to which multi-vector techniques are affected by the test conditions is variable and complex. Some tables with a qualitative analysis, based on the extensive empirical tests, contribute with a more complete insight and guide eventual control designers on the decision about the optimal regulation approach to be chosen. Full article

More and more distributed generations (DGs), such as wind, PV or battery bank sources, are connected to electric systems or customer loads. However, the locations of these DGs are based on the highest energy that can be potentially harvested for electric power generation. Therefore, these locations create different line impedances based on the distance from the DGs to the loads or the point of common coupling (PCC). This paper presents an adaptive virtual impedance (AVI) in the predictive control scheme in order to ensure power sharing accuracy and voltage stability at the PCC in a microgrid network. The reference voltage from mismatched feeder impedances was modified by utilizing the suggested AVI-based predictive control for creating equal power sharing between the DGs in order to avoid overburdening any individual DG with low-rated power. The AVI strategy used droop control as the input control for generating equal power sharing, while the AVI output was used as the reference voltage for the finite control set–model predictive control (FCS-MPC) for creating a minimum voltage error deviation for the cost function (CF) for the inverter’s vector switching pattern in order to improve voltage stability at the PCC. The proposed AVI-based controller was tested using two DG inverter circuits in a decentralized control mode with different values of line impedance and rated power. The performance of the suggested controller was compared via MATLAB/Simulink with that of a controller based on static virtual impedance (SVI) in terms of efficiency of power sharing and voltage stability at the PCC. From the results, it was found that (1) the voltage transient magnitude for the AVI-based controller was reduced within less than 0.02 s, and the voltage at the PCC was maintained with about 0.9% error which is the least as compared with those for the SVI-based controller and (2) equal power sharing between the DGs increased during the change in the load demand when using the AVI-based controller as compared with using the SVI-based controller. The proposed controller was capable of giving more accurate power sharing between the DGs, as well as maintaining the voltage at the PCC, which makes it suitable for the power generation of consumer loads based on DG locations for future energy communities. Full article