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

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.

Finite set model predictive control (FS-MPC) has become a promising control technology in power converter, because of the advantages of good dynamic response and accurate current tracking capability. In real-time control process, the conventional FS-MPC strategy requires more time for prediction and optimization. As a result, there will be a certain delay between sampling and output. In order to reduce the amount of calculation and overcome the adverse effect of the delay on the system performance, this paper presents a simplified FS-MPC method. Firstly, adjacent levels method is used to reduce the amount of the calculation, and then two-step FS-MPC is adopted to compensate the calculation delay. The control strategy is validated by the simulation and experimental results of a grid-connected cascaded H-bridge converter.

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) is a promising control scheme for power converters/inverters and electric drives due to its flexibility and simplicity. Using all the possible voltage vectors (VVs) generated by the inverter (7 VVs for 2-level inverter) and a discrete-time model of the system, the future behavior of the inverter can be predicted. Then, a certain cost function is employed for selecting the best switching action that will be applied in the next sampling interval (7 evaluations of the cost function are required). Accordingly, the main disadvantage of the traditional FCS-MPC scheme is the heavy calculation load. In this paper, an efficient FCS-MPC method for a grid-connected photovoltaic (PV) inverter is proposed in order to reduce the calculation burden of the traditional FCS-MPC. Based on the required reference current, the reference voltage vector (VV) is directly calculated. Then the sector where this reference VV is located is identified from its angle. Consequently, the cost function is evaluated only three times to obtain the optimal switching vector. The performance of the proposed efficient FCS-MPC is compared with the traditional FCS-MPC and voltage oriented control (VOC) schemes by simulation results for all operation conditions.

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.

The modular multilevel converter-based unified power flow controller (MMC-UPFC) is able to operate under unbalanced grid conditions with symmetric component decoupling. However, the constraint of the voltage limit of UPFC is not considered and no protection schemes are investigated to protect the UPFC from overmodulation under unbalanced grid conditions. To solve this problem, this paper proposes the cascaded control scheme for MMC-UPFC based on voltage limit control and symmetric component decoupling to balance the ac current of the transmission line. With appropriate transformer connections for MMC-UPFC, the negative- and zero-sequence currents are suppressed by the corresponding inner current loops. Considering the voltage limit of MMC, the operating ranges of UPFC-MMC under balanced and unbalanced grid conditions are investigated in both mathematical derivation and point scanning methods. The results are depicted and analyzed in 3-D vision. Based on the analysis of operating regions, the voltage limit control is proposed to protect the MMC from overmodulation and to maximize the controllable region under unbalanced grid conditions. Finally, the final cascaded control structure is constructed. The simulation results obtained in PSCAD/EMTDC are provided to validate the effectiveness and robust performance of the proposed control strategies.

This paper proposes a resonant-based backstepping direct power control (BS-DPC) strategy for doubly-fed induction generator (DFIG) under both balanced and unbalanced grid conditions. Proper formulae for the BS-DPC strategy are obtained based on the elaborated analysis of the mathematical model of DFIG. The influence of the unbalanced grid voltage on the normal BS-DPC is analyzed. Furthermore, a resonant-based improved strategy is proposed to achieve different control targets under unbalanced grid condition without the need of decomposition of positive- and negative-sequence components. Comparative experimental studies of the resonant-based BS-DPC and the normal BS-DPC for DFIG are conducted to validate the effectiveness of the proposed strategy under both balanced and unbalanced grid conditions. In addition, the experimental results also prove that the proposed resonant-based BS-DPC can achieve a satisfying transient performance.

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.

The modular multilevel converter (MMC) is an emerging and attractive topology for the high-voltage direct-current (HVDC) transmission system. This paper presents a generalized mathematical model for MMC in HVDC applications under balanced and unbalanced grid conditions. The dynamics of the positive-, negative-, and zero-sequence components are derived from the model. Then, a dual current control scheme with positive- and negative-sequence current controllers is applied to MMC. The power controller to eliminate negative-sequence current components and the other one to eliminate double-line-frequency voltage ripple are compared. Moreover, a zero-sequence current controller is proposed in addition to the positive- and negative-sequence current controllers. Time-domain simulations on a 61-level MMC-HVDC test system are performed in the PSCAD/EMTDC software environment. The results demonstrate that the MMC-HVDC system with or without converter transformer is able to operate under unbalanced conditions by the use of the proposed control scheme.

The Finite control set model predictive control (FCS-MPC) is recently introduced to control inverters without the modulation stage. The absence of the modulation stage gives an unpredictable performance of the control system. In this paper, the performance of FCS-MPC is assessed by comparing with PID control which is based on Scalar Pulse Width Modulation (PWM). The two control techniques are applied for load voltage regulation of the autonomous four-leg voltage source inverter (FLVSI). Practically, the predictive control requires a large number of calculations, resulting in high computation time and delay. In this paper, a new finite control set model predictive voltage control (MPVC) algorithm is proposed to predict the load voltages for 15 switching states instead of 16 switching states for reducing the computation time required for the control algorithm. Moreover, the algorithm is optimized by removing the repeated computations and the delay is compensated using the two-step prediction horizon principle. An accurate discrete-time state-space model of the autonomous FLVSI with output LC-filter is used for predicting the load voltages considering the neutral inductance and damping resistance of the LC filter. A simple PID control scheme with decoupled feedforward voltage and current loops is used in the DQ0 reference frame, while MPVC operates in the ABC reference frame. The simulation and experimental results are used to show the full assessment of the MPVC. The prominent outcomes show the ability of the proposed MPVC algorithm to provide high power quality under unbalanced and non-linear load conditions with high stability and robustness.

Finite control set model predictive control (FCS-MPC) of three-level neutral point clamped inverter-fed induction motor has received wide concern recently, thanks to its low switching frequency and fast dynamics. However, the performance largely depends on the accurate measurement and parameters which determine the feedback and control loops, respectively. Besides, FCS-MPC demands a large number of resources to perform the exhaustive search, which indicates that the computation burden increases exponentially with the increasing switching states. Aiming at improving the robustness under the condition of unavoidable measuring noises and parameter variation as well as reducing the computational burden, a passivity-based model predictive control (PB-MPC) scheme is presented in this article. For an ideal system, the PB-MPC has a similar character as that of the FCS-MPC. In real applications, where noises and disturbances impact the system more or less, PB-MPC outperforms FCS-MPC due to the power shaping and damping injection inherited from passivity-based control which ensures the asymptotic stability. A Lyapunov function is designed to prove the stability of the proposed scheme. The stability and efficiency are verified by both simulation and experimental results.

Model predictive direct power control for modular multilevel converter under unbalanced conditions with power compensation and circulating current reduction

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.