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Modular multilevel converters (MMCs) are used in medium and high power applications due to various advantages, such as modularity, scalability, high efficiency, and low THD. In this paper, an improved model predictive control (IMPC) is used to control the converter output current and the circulating current. Using a discrete mathematical model of MMC and the neighboring index values with respect to their previously applied values, the calculation burden can be reduced rapidly even the number of sub‐modules (SMs) increases. By applying this method, the control system complexity and the number of switching states are reduced. In addition, a Kalman filter (KF) is applied to estimate the capacitor voltage of the SMs, by using two arm voltage sensors in each leg of MMC. The combination of IMPC and KF reduces the computational burden and the number of voltage sensors. The efficiency and superiority of the proposed method are verified by simulation results of 5‐ and 11‐level three‐phase MMC and practical implementation of a laboratory single‐phase converter with four SMs in each arm. [ABSTRACT FROM AUTHOR]

Summary: Over the years, the solid oxide fuel cell (SOFC) is growing commercially due to its high fuel flexibility, lesser maintenance requirement, and environmental friendliness feature. However, its control is challenging due to its nonlinear behavior and simultaneous management of its operational constraints. Therefore, in this work, an adaptive model predictive control is designed systematically to handle the SOFC's behavior while optimizing its operational constraints such as fuel input, utilization factor, and change in pressure of hydrogen and oxygen. This model‐oriented control linearizes, augments, and discretizes the SOFC during run time while estimating the states using a time‐varying Kalman filter. Finally, it optimizes the control problem for predicting the internal states of the SOFC using successive linearization. The simulation results show that the proposed controller gives better performance of the SOFC in linear region, and significantly improved performance in nonlinear region in comparison to the conventional model predictive controller. The validation of the work has been done through real‐time simulation using OPAL‐RT's hardware setup. Using such control is beneficial as it enhances the efficiency of the SOFC significantly. Thus, this advanced controller is most suited for these kinds of nonlinear systems due to its superior performance. [ABSTRACT FROM AUTHOR]

Summary: Quasi Z‐source inverter (qZSI) is a single‐stage high gain buck‐boost converter suitable for various applications like renewable energy sources, power electronic drives, and power supply systems. Controller design for qZSI should ensure simultaneous control of multiple objectives such as the DC capacitor voltage, source inductor current, and load current. While controllers dealing with multiple objectives, obtaining a good steady‐state and transient performance with low control complexity is essential. Fortunately, Model Predictive Control (MPC) is emerging as an effective alternative that can address all the above‐said issues simultaneously. However, some of the severe problems to be addressed with the conventional MPC algorithms developed for qZSI are applying a single vector per sampling duration, higher computational requirements, and lack of analytic procedure to handle weight factors. This paper presents a control algorithm with features of weight factor elimination and AC load current ripple reduction by using a modified double vector predictive control approach with fewer computational requirements. The principle of inclusion of zero vector, weight factor elimination, and a detailed calculation for duty ratio are included. As the converter operates at a fixed frequency, significant ripple reduction for DC current, link voltage, and load current ripple have been achieved. The proposed MPC requires only ≈81% of computations needed for the conventional MPC. Methodological comparison has been provided to present the superiority of the proposed MPC compared to appropriate conventional MPCs available in the literature. [ABSTRACT FROM AUTHOR]

The adjusting of autonomous vehicles (AVs) steering angle represents a challenging issue in the intelligent vehicular applications. Among different control strategies, the model predictive control (MPC) is devoted to providing efficacious steering control for the AVs. However, the formulation of MPC with a few parameters is the main challenge to overcome the complicated computation that happens due to the long prediction and control horizons. Hybridization between discrete‐time Laguerre function (DTLF) and MPC is suggested to formulate the MPC with few parameters. While the hybrid DTLF–MPC requires an adequate tuning technique for their gains to improve the system performance. A new low computational burden design is proposed in this paper to adjust the gains of the hybrid DTLF–MPC based on the social ski driver algorithm (SSDA). This algorithm is developed as a recently artificial intelligence technique. The tuned hybrid DTLF–MPC by SSDA is devoted to control AV's steering angle. Furthermore, the proposed system takes into account the vision dynamics. The system performance based on the tuned DTLF–MPC by SSDA is evaluated with the MPC based on the neural network algorithm for comparison. Different scenarios are carried out to confirm the superiority of the suggested control mechanism to handle small as well as large‐scale variations of road curvatures. [ABSTRACT FROM AUTHOR]

Summary: This paper develops model predictive control with a finite control set (FCS‐MPC) for a static synchronous compensator (STATCOM) based on hybrid cascaded H‐bridge and neutral point clamped (HC‐HNPC) multilevel inverter. By applying FCS‐MPC to basic multilevel structures such as the cascaded H‐bridge (CHB), the dynamic performance is improved; but, as the number of output voltage levels increases, the method suffers from a high computation burden as a result of a significant number of switching states. In HC‐HNPC based STATCOM, the number of power switches and switching states is reduced compared to CHB STATCOM for an equal number of output voltage levels. As a result, while keeping the number of output voltage levels the same as CHB, the FCS‐MPC computational burden decreases, and the control objectives are achieved easier. Furthermore, HC‐HNPC STATCOM as a hybrid multilevel STATCOM contains different types of cells with different capacitors voltage. Utilizing the introduced FCS‐MPC, each capacitor voltage is kept balanced without using a complex balancing system. For a detailed investigation, a single‐phase 9‐level HC‐HNPC is employed in this research. The FCS‐MPC control is developed for 9‐level HC‐HNPC STATCOM and the mathematical equations are extracted. Finally, the control performance is verified by simulation and validated with experimental results. [ABSTRACT FROM AUTHOR]

Summary: With the new edge of smart grids technology, the concept of efficient energy management among cooperative microgrids (MGs) distribution networks for the next generation of energy trading applications has become more and more challenging. This article presents smart energy dispatch for multi‐MGs (MMGs) systems based on the cooperative distributed economic model predictive control (DEMPC) within a hierarchical structure. The optimization framework consists of two levels. First, a DEMPC coordinator computes the optimal energy dispatch solution, taking into account the energy surplus with a power‐sharing policy, the retail electricity price based on the time of use (TOU), and the distributed generators units. For the second level, each local controller executes the energy scheduling reference from the first level. Thus, the MMGs system achieves a global supply‐demand balance. Moreover, the proposed approach improves the utilization of renewable power and demonstrates optimal energy dispatch while reducing operating costs. Finally, to validate the efficiency and resiliency of our proposed approach, two cases of study are presented based on three interconnected MGs system under a cooperative and non‐cooperative optimization. Simulation results show the advantage of the cooperative strategy with 28% for MG 1, and 20% for MG 2 in terms of daily operating costs savings. [ABSTRACT FROM AUTHOR]

Summary: In this paper, model predictive control (MPC) of four leg distribution static compensator (FL‐DSTATCOM) is proposed to compensate the power quality issues and to reduce the higher neutral leg switching frequency of FL‐DSTATCOM. Current control using conventional MPC, selects a switching state which will minimize the single constraint cost function, comprising the difference between reference and actual DSTATCOM currents. However, FL‐DSTATCOM has a limitation of higher neutral leg switching frequency, and it can be reduced by adding an additional constraint to the cost function using a weighting factor. The reduction in neutral leg switching frequency also reduces the switching frequency of phase leg switches. But, the selection of weighting factor during multiple constraint case is a cumbersome task. Therefore, to simplify the weighting factor selection process, an optimization technique, namely, the technique for order of preference by similarity to ideal solution (TOPSIS) is applied, which further selects the optimal switching state. At the same time, compensation using FL‐DSTATCOM mainly depends on reference current extraction and it is a challenging task during unbalance and distortions in supply voltages. To address this problem, an improved conductance factor‐based approach is implemented in this paper, which makes reference current extraction simpler, effective and easy to interpret. The efficacy of the proposed method is evaluated using simulation and experimental studies. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel control drive for switched reluctance generator (SRG) driven from the wind power generation system. The SRG is controlled with a model predictive control (MPC) approach to generate the desired voltage applied to the phase winding being fed by a z‐source converter, for the first time. The converter itself is controlled with another MPC system to track the reference voltage of the previous section as fast as possible. To consider the nonlinear characteristics of the SRG, the flux linkage and electromagnetic torque have been derived from the finite element analysis of the machine and applied to the model. This novel drive maximizes the output power of the generator from three aspects. First, decreasing the rise‐up time of the phase current and the second, keeping the current in flat‐top mode at any speed and the third, maximum usage of the negative slope area of the phase inductance which is the generating zone. The whole of the system is simulated in MATLAB‐SIMULINK and the effectiveness of the proposed method is evaluated using the simulation results. [ABSTRACT FROM AUTHOR]

Wave energy is a renewable energy with a high density. There are different types of wave power generation systems (WPGSs), including Archimedes wave swing (AWS) coupled to a linear permanent magnet synchronous generator. This study proposes a model predictive control (MPC) for AWS‐based WPGS. The predictive current controller is implemented in a generator‐side converter as a substation for proportional integral (PI) controllers with pulse‐width modulation (PWM). Also, a supercapacitor energy storage system with bidirectional DC/DC converter is proposed to maintain DC link voltage stability. A DC/DC converter is used to keep DC microgrid bus voltage at the desired value. The effectiveness of the proposed MPC method is tested at various operating conditions and compared with conventional PI controllers with PWM in the MATLAB/SIMULINK environment. [ABSTRACT FROM AUTHOR]

Background: MC has many advantages such as no need for high‐value filters, capability of bi‐directional energy conversion and no need for DC bus. However, distortion in the source voltage, such as noise or harmonics may be reflected directly on the load side due to the lack of any storage elements. Purpose This paper proposes an effective method for finite control set model predictive control (FCS‐MPC) implemented in matrix converter (MC) to overcome these possible drawbacks and to improve source current quality under distorted source voltage conditions. Method: Proposed approach based on instantaneous unity power factor (IUPF) strategy is applied to compensate for disturbance in the source voltage. In order to achieve this, phase‐locked loop (PLL)‐based virtual source voltage is generated at the same frequency as the source voltage to obtain sinusoidal reference current to be used in FCS‐MPC. Design Proposed method is performed using TMS320F28335 Digital Signal Processor (DSP) that is a 32‐bit floating‐point processor operating at 150MHz. Results: Experimental results show that it is possible to obtain sinusoidal current waveforms on both sides despite the distortions in source voltage or harmonics due to the proposed virtual source voltage strategy. Conclusion: The results obtained from the experimental set‐up confirm the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Integration of batteries and supercapacitors (B‐SCs) is widely used to improve performance of electric vehicles (EVs). In this article, we consider the energy optimization problem of B‐SCs in EVs and propose an efficient model predictive control (MPC) algorithm for real‐time energy optimization of the hybrid energy storage system of EVs. Back propagation neural network is firstly adopted to learn the velocity prediction ability over a finite horizon by standard driving cycles. Then real‐time energy optimization of B‐SCs in EVs is formulated as the finite horizon optimal control problem by taking into account the constraints, the cost function on battery current, and the predicted velocity of the EV. Moreover, to lessen the computational burden of online solving the problem, the Pontryagin's Minimum Principle is used in a fashion of receding horizon. Compared with traditional nonlinear MPC, simulation results verify the effectiveness of the proposed MPC algorithm for real‐time energy optimization of B‐SCs in EVs. [ABSTRACT FROM AUTHOR]

Summary: With the analysis on the drawbacks of the traditional direct power control for three‐phase pulse width modulation (PWM) rectifier, including the unfixed switching frequency, the higher grid‐side current harmonic component, and the longer system setting time, one method of the model predictive direct power control based on error correction for three‐phase voltage source PWM rectifier is proposed in this paper. Based on instantaneous power theory, the PWM rectifier model predictive objective function of is built. Its partial derivative is calculated to predict the variations of active and reactive power in the next sampling period and achieve the tracking control for the given power of the next period. The second‐order Lagrange interpolation method is introduced to correct the predict power to achieve the minimum error of active and reactive power between actual value and the predicted value. Instead of switch table, space vector PWM is adopted to drive the power switch of rectifier. This method simplifies power control inner‐loop structure and is able to achieve the fixed switching frequency. The experimental results show that this method has better dynamic and static performance; the system is not sensitive to the change of inductance parameters; it can reduce the total harmonic distortion of grid current effectively and improve the power factor. [ABSTRACT FROM AUTHOR]

High switching loss remains a predominant issue in conventional finite control set model predictive control (FCS-MPC) for voltage source inverter, aside from the uneven distributed loss between upper switches and lower switches. While maintaining the inherent simplicity and effective reference tracking capability of conventional FCS-MPC, a new FCS-MPC is proposed to acquire substantial improvement in switching loss reduction by choosing the optimal zero vector and two active vectors. Selection of the two active vectors is performed by considering two legs that have maximum and minimum output voltage at particular sampling instant, while the selection of the optimal zero vector is based on the switching states of the previous sampling instant. The proposed method also ensures uniformly distributed loss in each phase as well as between upper switches and lower switches. Further switching loss reduction by selecting the appropriate weighting factor in the cost function is investigated for both the conventional and proposed methods as well. A loss model is constructed in MATLAB/SIMULINK for analyzing quantitative switching loss, conduction loss, total harmonic distortion, and number of switching. The proposed method ensured its superior performance under different load angles and sampling periods, and all the results are verified through simulation. [ABSTRACT FROM AUTHOR]

Summary: A model predictive current control (MPCC)‐based single‐phase shunt active power filter (SAPF) is proposed in this manuscript to improve power quality by harmonics and reactive power compensation. The DC link capacitor voltage regulation‐based PI control algorithm is used for the computation of SAPF reference current signal. The current commands are tracked using the MPCC. In this manuscript, the control technique effectiveness is demonstrated through MATLAB simulation and experimentation with Cyclone‐IV EP4CE30F484 FPGA board under dynamic operating conditions. The obtained results from a simulations and hardware prototype shows the proposed algorithm shows perfect reference current tracking at every sampling instant and excellent dynamic performance. Subsequently, the main advantages of this proposed controller are absence of internal current control loops and modulation stage. [ABSTRACT FROM AUTHOR]

Summary: Model predictive control (MPC) is becoming a popular topic in the control of AC motor and power electronic converters because of its high dynamic performance and multitarget optimization ability. However, steady‐state error still exists in conventional MPC method because of unavoidable errors of model parameters. In addition, time delay in digital control also affects the performance, and even stability of MPC. In this paper, an offset‐free robust model predictive control (OFR‐MPC) method is proposed for the current control of three‐phase induction motor (IM). An incremental model is adopted to improve the parameter robustness and to realize offset‐free prediction. An improved current observer is designed to realize time‐delay compensation in control variable optimization. In addition, the improved current observer is also acting as a filter to solve the problem of noise amplification effect of the incremental model. The proposed method can also be applied to other AC motors and power electronic converters. Simulations and experiments have been implemented to verify the performance of the proposed OFR‐MPC method. [ABSTRACT FROM AUTHOR]

Summary: On the basis of model predictive control and auxiliary problem principle, this paper proposes a practical distributed framework to solve the economic dispatch of microgrid clusters (MGCs); specifically, each microgrid solves its local model in a parallel way and cooperates with adjacent microgrids in an iterative way. Besides, incentive‐based demand response is incorporated in optimal dispatch problem in order to enhance the flexibility of demand side. The proposed method aims to make a balance between the profit of microgrid system operator and customer's experiences for a typical MGC by setting a proper trade‐off parameter. Specifically, the framework is able to make sub‐microgrids plug in and plug out freely and has fast iterative speed. The simulation results show that a few of the iterations are needed to obtain the optimum by using the proposed framework and, meanwhile, verify the effectiveness of plug‐and‐play functionality. [ABSTRACT FROM AUTHOR]