Efficient finite control set-model predictive control for grid-connected photovoltaic inverters

In this paper, a Lyaupnov-based finite control set model predictive direct torque control for the permanent magnet synchronous machine (PMSM) is proposed. In the proposed control scheme, the finite control set prediction and the Lyapunov theory are combined to minimize the torque ripple. The eight voltage vectors of the two-level converter are utilized as a finite control set for the torque prediction of the PMSM. A cost function considering the torque error, the maximum torque per ampere operation and the current limitation is introduced. Comparing to the conventional finite control set predictive control, the dominant part of the cost function is utilized as a Lyapunov function to estimate the duty cycle of each voltage vector. An optimum voltage can be obtained by the optimum voltage vector from the eight vectors and their duty cycles. A small sampling frequency and a fixed switching frequency can be realized when compared to the conventional finite set model predictive control. In the end, the simulation and experimental results validate the performance of the proposed control scheme.

A direct torque control (DTC) with a modified finite set model predictive strategy is proposed in this paper. The eight voltage space vectors of two-level inverters are taken as the finite control set and applied to the model predictive direct torque control of a permanent magnet synchronous motor (PMSM). The duty cycle of each voltage vector in the finite set can be estimated by a cost function, which is designed based on factors including the torque error, maximum torque per ampere (MTPA), and stator current constraints. Lyapunov control theory is introduced in the determination of the weight coefficients of the cost function to guarantee stability, and thus the optimal voltage vector reference value of the inverter is obtained. Compared with the conventional finite control set model predictive control (FCS-MPC) method, the torque ripple is reduced and the robustness of the system is clearly improved. Finally, the simulation and experimental results verify the effectiveness of the proposed control scheme.

Due to the large number of candidate voltage vectors, there is heavy calculation burden of finite control set-model predictive control (FCS-MPC) in multiphase open-winding (OW) motor drive systems. Though low-complexity FCS-MPC schemes exist, they are sensitive to motor parameters. In this article, a robust FCS-MPC with high computational efficiency is proposed for the nine-phase OW-PMSM. First, based on the virtual voltage vector scheme synthesized by three active vectors, harmonic voltage components are eliminated. Then, the deadbeat current control is introduced to calculate the reference voltage vector (RVV), and the optimal voltage vector is determined by the location of the RVV, which successfully reduces the number of iterations in selecting the voltage vectors and improves the computational efficiency. In addition, in order to mitigate the sensitive of the proposed scheme to motor parameters, a parameter extraction method based on a sliding mode observer is designed to enhance the robustness of the FCS-MPC. Moreover, the corrected parameters can also be used for other calculations to achieve satisfactory steady-state performance, such as duty cycle optimization. Finally, experimental results verify the feasibility and superiority of the proposed method.

Finite control set model predictive control (FCS‐MPC) has been widely used in the control of grid‐connected converters with the advantages of fast dynamics, multi‐objective control, and easy implement. However, the conventional FCS‐MPC bears with variable switching frequency, high current ripple and computational burden. An improved current model predictive with the cost function‐based modulation scheme (CFM‐MPC) is proposed for a three‐phase three‐level VIENNA rectifier to improve the power quality. First, the mathematical model and voltage vector are given according to the principle of deadbeat control. Then, the voltage vector of different voltage vectors are selected according to the location of the voltage vector reference, and the switching action time of the selected are directly calculated by the inversely proportional with cost function value of the selected vectors. It remains the merits of both the conventional MPC and space vector pulse width modulation schemes to track the optimum voltage vector without increasing the computational burden. Finally, a comparative study with the proposed CFM‐MPC and conventional FCS‐MPC has been conducted to verify the superiority of the proposed scheme. The results show the proposed CFM‐MPC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion and neutral point potential balance.

For the purpose of improving the performances of T-type neutral point clamped (T-NPC) photovoltaic (PV) grid-connected inverter under large grid, a passivity-based decoupling control scheme is proposed in this paper. First, the Euler-Lagrange (EL) mathematical model of a T-NPC PV grid-connected inverter with an LC filter is built based on the power circuit and operating principles. Second, with this EL mathematical model and passivity of the inverter, the passivity-based controller is designed by the damping injecting method. Third, by adopting a proportional integral regulator to directly control direct current (dc) voltage, together with passivity-based indirect control, better performances of the dc voltage control can be further obtained. With the proposed passivity-based controller, the PV grid-connected inverter can achieve both dynamic current decoupling under the synchronous rotating dq coordinate and dc side voltage dynamic decoupling, which can improve the quality of the current connected into the power grid. The passivity-based PV grid-connected inverter is with strong robustness against parameters perturbations of resistors and attenuating the influence of delay in the inverter. Moreover, the passivity-based decoupling control scheme proposed in this paper for the T-NPC PV grid-connected inverter has a simple structure, and is easily debugged and realized in engineering. Experimental results under 10-kW prototype show that the proposed control scheme is effective.

This work introduces the control concept of variable sampling time finite control-set model predictive control (FCS-MPC). The new control concept is introduced in theory based on a review of the conventional FCS-MPC concepts performed with a constant sampling time. Based on the partitioning of the sampling instant in multiple smaller sampling instants it is possible to optimize, besides the switching states, the switching states turn-on times. Therefore, the proposed variable sampling time FCS-MPC sets both: the switching state and the related turn-on times. To utilize the available calculation power for longer sampling instants an adaptation of the control- and prediction horizon to the sampling time is proposed. The theoretical control concepts are applied to the control of a grid connected two-level voltage-source converter where a simple L-type line-filter is used to demonstrate the control performance of the variable sampling time FCS-MPC algorithms in the laboratory environment.

This paper proposes a model predictive control based on three voltage vectors for an interior permanent-magnet synchronous motor in a rotating reference frame. The proposed method can obtain the reference voltage vector quickly by predicting only one voltage vector during one sampling period using the characteristics of the deadbeat control. In addition, to obtain better steady-state performance, the optimal vector duration ratios can be obtained from the cost function to minimize the error between the predicted reference voltage vector and the synthesis vector by using the switching sequence relationship of a symmetrical three vector, which is different from applying a single voltage vector in the conventional finite-control-set model predictive control (FCS-MPC). To verify the proposed method, the experimental results are compared with the conventional FCS-MPC and two-vector-based MPC.

This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

An improved finite control-set model predictive control for nested neutral point-clamped converters under both balanced and unbalanced grid conditions

This paper presents a different Finite Control Set – Model Predictive Control (FCS-MPC) for grid-connected three-phase bidirectional power inverters. These are typically used in dc or ac renewable-based microgrids (MGs), where bidirectional operation and fast dynamic response is required. The bidirectional grid-connected inverters are an essential part of MG, which inject energy into the ac grid or demand energy from it. The dynamic behavior of the system is a major concern since the current can suddenly change depending on the hierarchical controller. This paper proposes a different cost function using sliding mode theory, which offers a good dynamic response, reduced computational burden, and a parameter-free control model. The operation principle of the proposed controller is given and evaluated using a Hardware-In-the-Loop (HIL) system, but also experimentally with a 1kW laboratory prototype. The final results demonstrate the advantages of using this approach in grid-connected three-phase bidirectional power inverters in terms of dynamic response and reduced computational burden, making this solution technically attractive and viable.

The Finite Control Set Model Predictive Control (FCS-MPC) when applied for voltage control in a Boost converter presents limitations due to the non-minimum phase characteristics of this system (NMP). Therefore, setting an adequate cost function is essential for the success of the control strategy in short-time prediction horizons. In this work, the performance of the FCS- MPC applied to the Boost converter is compared among different cost-functions configurations. In a first scenario, the traditional voltage cost function is compared with two distinct proposals made by the authors: a multivariable voltage and current cost function, and a current-based function that perform indirect voltage control. In a second scenario, model corrections are made as proposed by Villarroel et al. (2019) for the delays caused by the non-minimum phase. The converter is software-simulated using Matlab/Simulink and the results obtained shows that the use of the multivariable cost function alone does not solve the optimization problem caused by the non-minimum phase characteristic. However, when the system is indirectly controlled using current control, or else when is operated with FNM corrections, the FCS-MPC demonstrated to be able to optimize the system output with low steady-state errors. Furthermore, the use of the multivariable cost function with corrections slightly improves the output voltage oscillations while maintaining a very fast transient response characteristic.

The main drawbacks of traditional finite set model predictive control are high computational load, large torque ripple, and variable switching frequency. A less complex deadbeat (DB) model predictive current control (MPCC) with improved space vector pulse-width modulation (SVPWM) under a single-phase open-circuit fault is proposed. The proposed method predicts the reference voltage vector in the α-β subspace by employing the deadbeat control principle on the machine predictive model; thus, the exhaustive exploration procedure is avoided to relieve the computational load. To perform the constant switching frequency operation and achieve better steady-state performance, a modified SVPWM strategy is developed with the same conventional structure, which modulates the reference voltage vector. This new approach is based on a redesigned and adjusted post-fault virtual voltage vector space distribution that eliminates the y-axis harmonic components in the x-y subspace and ensures the generation of symmetrical PWM pulses. Meanwhile, the combined merits of the DB, MPCC, and SVPWM methods are realized. To verify the effectiveness of the proposed control scheme, comparative experiments are performed on a five-phase permanent magnet synchronous motor (PMSM) drive system.

Deadbeat control (DBC) has been known for decades due to its conceptual simplicity and good performance. Recently finite control set model predictive control (FCS-MPC) is emerging as a powerful strategy for control of power converters. Different from DBC, which uses a modulator such as space vector modulation (SVM), FCS-MPC directly obtained the optimal switching states of power converters by minimizing a cost function. Several improved versions of FCS-MPC have been proposed to improve the steady state performance by introducing two or three vectors during one control period. This paper investigates the inherent relationship between SVM-DBC and multiple-vector FCS-MPC. It is found that with the aim of tracking error minimization, the two kinds of predictive methods can be unified in one frame, where the vector sequence and duration in FCS-MPC can be easily obtained from SVM-DBC. In other words, under some special cases, FCS-MPC and its improved versions can be considered as special cases of SVM-DBC. This is the main contribution of this paper, because DBC and FCS-MPC are previously studied separately. The theoretical study is confirmed by the analysis on state-of-the-art DBC and FCS-MPC and supported by the selected simulation study and experimental tests from a three-phase two-level voltage source PWM rectifier.

This paper deals with a particular problem in the conventional finite set model predictive control (FS-MPC) method in VSI drives. When FS-MPC is experimentally implemented, the controller chooses any one of the eight available voltage vectors. Therefore, if the controller selects any of the far states, simultaneous switching events among the inverter legs occur. This causes line-line voltage polarity reversal (LLVPR) with the voltage change twice the value of the DC-link voltage. The fast LLVPR combined with the reflected wave by the motor cable can damage the insulation of the stator winding. In this letter, the problem of LLVPR in electric drives with FS-MPC is described, and the solution to this issue is proposed. Experimental results to validate the proposed method are shown.

This paper proposes a torque control method for surface mounted permanent magnetic synchronous motor (PMSM) driven by a 2-level voltage source driven inverter, which has fast torque response and small torque ripple. The proposed torque control method follows the finite control set model predictive control (FCS-MPC) strategy. A reference state is derived at each time step for the given time varying torque reference and the cost index is defined so that the tracking error for this reference state should be penalized. The choice of an optimal output voltage vector is made first from the 6 possible active voltage vectors of the 2-level voltage source inverter. Then a modulation factor for the chosen optimal voltage vector is obtained so that the torque ripple can be reduced further. It is shown that the proposed FCS-MPC control method yields fast torque tracking response and small torque ripple through simulation and experiments.