Predictive Current Control Method for Photovoltaic Energy Storage Bidirectional DC-DC Converter

To improve the dynamic and steady-state control performance of permanent magnet synchronous motors under the three-vector model predictive current control method, this study proposes a switching current predictive control method based on the exponential moving average algorithm, which evaluates the magnitude of the change of the q-axis current slope in real time to discriminate the motor’s operating conditions and selects the optimal control method for different operating conditions. Meanwhile, the traditional three-vector model predictive current control method is improved by introducing a comparison mechanism for the q-axis current slope to select the second effective voltage vector, avoiding the secondary optimization calculation of the value function and reducing the computational complexity of the traditional method. By comparing the proposed method with the traditional three-vector model predictive current control method, the experimental results prove that the proposed method improves the system’s dynamic response and steady-state performance.

For multiobjective model predictive current control (MPCC) method with penalties, the penalty weights are introduced into the cost function to determine the relative importance of control targets. However, the dimensions and value ranges of each control target are different. Thus, the penalty coefficient is difficult to determine. To avoid the difficulty of dimensionless parameter design, this article proposes a multiobjective control method called MPCC with multiple current bounds. In this article, the switching frequency and the common-mode voltage (CMV) are selected as the control target. Their control is converted into the limitation of the current ripple so that the control parameters have a clear physical meaning. On the one hand, the proposed method can realize the cooperative control of reference current tracking, switching frequency suppression, and CMV suppression. On the other hand, the key control parameters have the same dimension and strong correlation with others. Therefore, appropriate parameter design can be realized. Experimental investigations for permanent magnet synchronous motor prove the effectiveness of the control method.

Switching losses minimization and performance improvement of PCC and PTC methods of model predictive direct torque control drives with 15-level inverter

To address the complexity of vector selection in the conventional predictive current control strategy and the reduction of control system robustness when the motor parameters change, this paper proposes a finite-set model-free three-vector predictive current control method for permanent magnet synchronous motor based on the improved sliding-mode perturbation observer. According to the mathematical model of a permanent magnet synchronous motor under perturbation, a new hyperlocal model is established, and the conventional vector traversal optimization method is optimized to obtain the necessary basic voltage vector by analyzing the function with one judgment. Meanwhile, the sliding mode perturbation observer is designed using the fast exponential convergence law to observe the unknown part. Finally, by experimentally contrasting the proposed technique with the conventional three-vector model predictive current control, the method suggested in this paper can successfully reduce the current pulsation and inhibit the system perturbation caused by the parameter change, ensuring the steady state performance of the motor, as demonstrated by the experimental comparison with the conventional three-vector model predictive current control method.

The five-phase voltage source inverters (VSIs) have drawn more and more attention in the recent years, and required the extension of control schemes already well known for three-phase voltage source inverters. Nevertheless, the low-order harmonic currents suppression is a special issue of control methods for five-phase VSIs with the common-mode voltage (CMV) still remaining to be reduced. Model predictive current control (MPCC) method has been developed as a widespread and effective control technique in power electronics, especially for multiple constraints system, and has been applied to five-phase VSIs with the consideration of low-order harmonic currents suppression and CMV reduction, simultaneously. However, the complex cost function and huge computation amount in the existing works become another deficiency, and the weighting factors of different components in the cost function is difficult to adjust. In this paper, a new MPCC method based on virtual voltage vectors has been proposed for five-phase VSIs. The zero and medium voltage vectors have been eliminated and by this way the CMV can be reduced by 80%. Then, following the volt-second balance law, ten active virtual voltage vectors and active zero vectors are synthesized to acquire zero volt-second value in x-y subspace, as a consequence, low-order harmonic currents are suppressed. Finally, a simplified cost function without weighting factor is adopted. Simulation and experimental results have verified that the proposed MPCC method can reduce CMV by 80% and suppress low-order harmonic currents successfully.

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

The general model predictive current control uses a discrete-time model of the system to predict the future current behavior for all the possible voltage vectors generated by the inverter, and then the vector which minimizes a cost function is selected and applied. In this paper, a simplified model predictive current control method for a three-phase voltage-source inverter is presented in order to reduce the amount of calculations in the practical implementation of this method, especially for multilevel ones with large predictions. Utilizing the sector information of the reference voltage space vector, the proposed method just needs a subset of all the available voltage vectors for the prediction and optimization. The computational effort has been greatly reduced while the steady-state performance and dynamic response of the current control are improved. In addition, a reduced switching frequency is obtained. Simulation results are presented to verify the proposed method.

During the past few years, finite control set predictive current control (PCC) method has attracted more and more attention in research and industry applications. However, PCC method could be improved by considering two points. First, the current reference used in the cost function of PCC control scheme is usually produced by a proportional and integration speed controller. Confront with load torque and time-varying system parameters, it is a better solution to design a disturbance estimation based feed-forward compensated controller. In this way, the current reference could be generated faster and more accurate. Second, the PCC method is model-based method which means the accuracy of the model parameters are essential. In real system, time-varying parameters existed almost always. This paper investigates a generalized proportional integral observer based PCC approach for dealing with load torque disturbance, time-varying parameter uncertainties. The effectiveness of the proposed method has also been confirmed by simulation and a lab-constructed experimental prototype.

This paper presents a reduced control set model predictive control (RCSMPC) method for three-phase T-type neutral-point-clamped (NPC) inverter. The whole control set (WCS) consists of all the 27 switching states of T-type NPC inverter. The reduced control set (RCS) with 19 switching states is formed from WCS by excluding the switching states with common mode voltage (CMV) value higher than one-sixth of input DC voltage [Formula: see text]. With RCS, single-objective model predictive current control method can restrict the CMV peak value to [Formula: see text]. To further reduce the CMV below this threshold, a cost function with the weighted sum of two control targets is formulated in the RCSMPC method. The two control targets of RCSMPC method are CMV mitigation and load current control. The weight for CMV is called bias factor. The RCSMPC method is computationally efficient, as the number of switching states is less than that of WCSMPC. To further reduce the computational burden, CMV values corresponding to all the switching states are calculated offline and stored in memory. Robustness of both the methods is investigated with parameter deviations at different bias factors and reference currents. The proposed method is validated using simulation and experimental results and compared with the existing methods.

This paper proposes a model predictive current control (MPCC) method with duty cycle control based on the Runge–Kutta approximation compared to the Forward Euler Approximation for grid-connected three-phase inverters with output LCL filter. Hence, results of proposed MPCC methods alongside of the conventional MPCC have been investigated to find the best strategy. First, all 7 possible switching states have been checked by the discrete-time system model based on two approximation methods to select a state that minimizes the cost function. However, at this stage only one voltage vector is chosen during one control period, which cannot decrease the current ripples to a minimum value. Hence, for having sufficient performance, the sampling frequency is necessary to be selected high. Then, the idea of duty cycle optimization has been introduced by using two voltage vectors (a non-zero and a zero voltage vector) during one control period. Therefore, the duration of two voltage vectors have been defined according to the principle of current error minimization. Finally, the effectiveness of the proposed MPCC method based on the Runge–Kutta approximation has been verified by MATLAB/Simulink and experimental results exhibit a better steady-state performance with less sampling frequency as compared to the conventional strategy.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

This paper proposes a model predictive current control (MPCC) method utilizing two output voltages with variable application durations in one sampling period, to control output currents of single-phase voltage source inverters (VSIs). In the proposed method, application durations of the two voltages as well as selection of two output voltages used in the future sampling period are determined by an optimization process, to minimize current error inside the future sampling period and to eliminate current error at the end of the future sampling instant.

Aiming at the problem of difficult allocation of the dq-axis current term weight coefficients of the cost function in the traditional finite control set model predictive current control of a permanent magnet synchronous motor, a finite control set model predictive current control method based on a neural network to optimize the cost function is proposed. In this paper, the neural network is constructed to assign the weights of the cost function by judging the changes of the speed deviation and the speed change rate, and the method improves the dynamic performance of the system and reduces the influence of the unreasonable assignment of the dqaxis weight coefficients on the system. The proposed method is verified by simulation analysis.

Investigation of the wave–photovoltaic-battery​ hybrid power generation platform’s control strategy

In this paper, a model predictive current control (MPCC) method based on pseudo-dq transformation is proposed for maximum torque per ampere (MTPA) control of the brushless dc motor (BLDCM). A current predictive model of BLDCM in discrete steps is built. A cost function considering the torque current tracking and the MTPA operation is introduced. The optimal voltage vector to control the motor is then selected according to the cost function. Additionally, to improve the robustness of the system, a sliding mode control is applied to the outer speed control loop. In the end, the simulation results validate a better performance of the proposed control scheme compared to the traditional control method.