The three-phase grid-connected inverters are widely employed in various distributed generation (DG) applications. The use of LCL filters at their outputs has proven several benefits, regarding passive component size reduction and improved high-frequency harmonics elimination. However, the resonance of the LCL filter hinders their application. Another issue with applying model predictive control (MPC) in LCL-filter-based DGs, as a high-performance controller, is the difficulty in including the various control objectives in a single cost function. The main issues include the necessity to compensate for various delays in the control loops and the selection of a proper weighting factor between objectives. Therefore, this paper proposes an efficient MPC method for grid-connected LCL-filtered DG inverters. The proposed MPC employs proper delay compensation for current/voltage loops. Furthermore, the proposed method incorporates various controlled variables in a single-objective function, eliminating the need for cascaded control loops. The proposed MPC is simulated in MATLAB/Simulink, and different test cases of active/reactive power demands are provided. The obtained results show the robust resonance-free performance of the proposed MPC method.

This paper presents a robust control strategy combining Space Vector Modulated Model Predictive Control (SVM2PC) with an Extended Kalman Filter (EKF) for online parameter estimation in grid-connected inverters. The proposed method enhances controller adaptability by rendering it independent of precise system parameter knowledge. The EKF continuously estimates key system parameters, enabling the predictive controller to perform accurately despite inductance variations. Simulation results demonstrate that, compared to conventional SVM2PC, the proposed EKF-enhanced approach achieves superior current tracking, reduced total harmonic distortion (THD), and preserved transient performance and stability, highlighting its effectiveness in improving control robustness under parameter uncertainties.

This paper presents a Continuous Control Set Model Predictive Control (CCS-MPC) enhanced with Nestorov Accelerated Gradient (NAG) for real-time closed-loop control of Cascaded H-Bridge (CHB) converters under normal operation and with faults on the power cells. The proposed method addresses limitations of conventional Finite Control Set MPC (FCS-MPC), including variable switching frequency and the computational burden for the control of multilevel converters. The main contributions of the proposed CCS-MPC with NAG are: (i) derivation of a dynamic model that enables simultaneous control of line currents and common-mode voltages under normal and fault conditions; (ii) formulation of the CCS-MPC problem as a convex, box-constrained optimization, enabling the use of an efficient projection-based solution within each NAG iteration to enforce feasibility; (iii) offline computation of matrices to minimize online computational burden; and (iv) a closed-loop reference voltage control to ensure fault-tolerant operation. The effectiveness of the proposed CCS-MPC with NAG is validated through real-time Hardware-in-the-Loop (HIL) simulations, that confirm the fast dynamic response and robust performance of the proposed algorithm under fault conditions and demonstrate its suitability for high-performance and reliable operation for industrial drives and grid-connected applications.

This paper presents a hybrid controller based on fractional order control (FOC) with finite control set model predictive control (FCS-MPC) for LCL grid-tied single-phase multilevel inverters. Recently, FCS-MPC has received major interest in fast control tracking and handling several control objectives together in a single cost function. However, in the case of LCL grid-tied inverters, the control of the inverter side current, grid side current, and AC filter capacitor voltage, in addition to the voltage balance of dc-link capacitors, makes the design process of a single cost function more difficult to reach. Therefore, a cascaded FOC resonant controller for grid-side current with inner FCS-MPC for controlling inverter-side current and capacitor voltages is proposed for a single-phase five-level H-bridge T-type grid-tied inverter. The proposed controller merges the characteristics of FOC, FCS-MPC, and resonant controllers, improving system performance. Simulation results are provided to verify the superior tracking and control performance of the new proposed controller.

This article presents an optimization in implementing a DTC control scheme using a 3L-NPC inverter to control the speed of a three-phase induction machine. Regarding the DTC strategy, 3 and 5-level hysteresis controllers were used to control the stator flux and electromagnetic torque, respectively. The traditional implementation of the scheme [2], shows good results regarding the motor speed response; however, the electromagnetic torque developed by the motor presents various issues: significant ripples in torque produced by a poor choice of the hysteresis controller amplitude, poor tracking of the torque reference signal in steady state, and high torque peaks in large speed steps, which translate into current peaks that exceed the motor’s thermal limit. This work proposes new strategies for adjusting the flux and electric torque hysteresis controllers, which will correct the problems presented in the traditional implementation. Finally, a comparison will be made between the modified DTC strategy and the traditional DTC strategy of [2].

This paper proposes an optimal Finite Control Set Model Predictive Control for a three-phase Neutral Point Clamped inverter, considering its switching constraints. Initially, an optimization problem is solved to obtain the inverter line-to-line voltage vector that minimizes the current tracking error. Here, the constraints of the optimization problem are the lines that delimitate a convex hull around the last implemented vector, where the candidate vector presents at least one switching state which satisfies the switching constraint of the NPC inverter. The NPC’s redundant phase-voltage vectors are generated according to the switching constraint to avoid the potentially destructive switching transitions, and a cost function is then evaluated to balance the DC bus neutral point voltage, as the second control stage. As a result, the proposed Optimal FCS-MPC avoids the design of weighting factors and has the advantages of optimal performance, fast transient response, and superior steady-state performance, compared to similar algorithms. Finally, simulation results are presented to compare the proposed Optimal FCS-MPC algorithm with other FCS-MPC strategies presented in the literature, and the effectiveness of the proposed algorithm is demonstrated.

Recently, doubly grounded (DG) photovoltaic (PV) inverters have proven superior performance in total elimination capability of PV leakage currents. This is due to the direct connection of PV side negative terminal with grid side neutral terminal. This paper presents a boost DG-PV five-level (5L) inverter topology based on neutral point clamped (NPC) leg. The proposed DG-PV boost 5L-NPC inverter totally eliminates the existing leakage currents, achieves boost of PV voltage, is considered a single stage PV inverter structure, uses low component count, and has continuous PV current. Moreover, finite control set model predictive control (FCS-MPC) is presented for controlling multiple objectives in a single cost function for the proposed topology. The FCS-MPC also eliminates the cascaded l0ops in the conventional control methods, which facilitates the control design process. A simulation based case study is preformed of the proposed DG-PV boost 5L-NPC inverter with FCS-MPC controller. Superiority of proposed inverter and controller have been verified with various step changes in PV power, power quality of injected grid current, and capacitor voltage control.

The rapid growth of smart grids necessitates advanced control strategies to ensure efficient and reliable power distribution. This paper proposes a hierarchical control framework based on Model Predictive Control (MPC) for managing power distribution in a smart grid. The hierarchical structure comprises three control levels: the first hierarchy is to control the inverter variables and the LC filter voltage, the second hierarchy supervises the power control restriction for every inverter, and the hierarchy is for power coordination, which utilizes long-term condition and prediction and power supply restriction (solar or wind capability, among other conditions or restrictions). The simulation results presented in this paper validate the fast control response and the capability of coordination between the different hierarchies of the proposed strategy.

This paper presents a novel hierarchical control scheme based on Model Predictive Control (MPC) for a Voltage Source Inverter (VSI) operating under conditions of unbalanced and nonlinear loads. The increasing prevalence of renewable energy sources and the proliferation of power electronic devices has led to a growing need for robust control strategies that can effectively handle the challenges associated with unbalanced and nonlinear loads in power distribution systems. In this work, a two-hierarchy control structure is proposed, based on a full MPC strategy. The MPC controller in the first hierarchy is responsible for optimizing the VSI's switching states in real-time, taking into account the system's constraints and the desired performance criteria of the output voltage of the VSI. The MPC controller in the second hierarchy regulates the output current of the VSI to track the reference values for the load and grid power. The proposed control scheme addresses the challenges posed by unbalanced and nonlinear loads, the simulation results demonstrate the effectiveness of the proposed hierarchical control strategy in mitigating voltage deviations, minimizing harmonic distortions, and ensuring balanced operation in the presence of unbalanced and nonlinear loads. The presented approach offers a promising solution for enhancing the performance and stability of VSI-based power distribution systems, thus contributing to the overall reliability and efficiency of modern electrical grids.

This article proposes a space vector modulated model predictive control (SVM$^{2}$PC) for voltage source converters. It overcomes limitations of standard finite control set model predictive controller, ensuring fast dynamic response, fixed switching frequency, and low computational burden. It combines control and modulation in a convex optimization problem with affine inequality constraints to minimize the tracking errors of the output variables. First, the sector of the space vector diagram where the unconstrained solution lies is identified. Then, using the Karush–Kuhn–Tucker conditions, feasible duty cycles are computed for both linear and overmodulation regions. The proposed SVM$^{2}$PC is extended to multilevel converters. Hardware in the loop results are presented for three different case studies: 1) Grid-forming inverter withLCfilter; 2) two-level grid-following inverter with LCL filter; and 3) grid-following neutral point clamped inverter with LCL filter. Experimental results demonstrate that the proposed SVM$^{2}$PC provides good performance, fast response, and low computational burden when compared with previously reported alternatives.

This paper proposes a Master-Slave Model Predictive Control for parallel grid-tied inverters in a microgrid. In this configuration, the Master is a grid-forming inverter with a Battery Energy Storage System as dc input, while the Slave is a grid-following PV inverter that provides the power to the load. First, the inverter models are derived and then their MPC controllers are presented in details. Finally, Hardware-in-the-Loop results are presented for different operational conditions for the microgrid, including grid connection, islanded mode and load variations. The results demonstrate the advantages of the proposed MPC such as faster dynamic response, multi variable control, adequate load sharing between the inverters, robustness to parametric uncertainties and variations and the possibility to directly include system constrains and non-linearities in the controller implementation.

This paper proposes a Geometrical Modulated Model Predictive Control (GM 2 PC), for grid-connected converters, ensuring voltage synthesis and current limiting with fast dynamic response, low computational burden and fixed switching frequency, also addressing the overmodulation issue. A discrete-time model for a converter with LC output filter and external disturbances is derived. Then, a convex optimization problem with two quadratic inequality constraints is formulated for minimizing the output tracking error. To solve this problem, each constraint is considered to be a circular area over the space vector diagram for voltage synthesis and current limitation. Using the Karush–Kuhn–Tucker conditions, an unconstrained solution is obtained in order to identify in which mode the proposed method should operate. Finally, a Hardware-in-the-Loop validation for a three-phase two-level inverter is presented. The results demonstrate that the proposed method is capable of providing good performances with a fast and efficient control algorithm.

This paper investigates the impact of the dead-time compensation on fixed frequency space vector modulated model predictive control for grid-tied inverters. Since grid-tied inverters with LCL filters may have inverter-side currents with large high-frequency current ripple, multiple zero crossings should be properly considered for dead-time compensation. Therefore, this paper proposes a method to predict the current at the switching instants within the sampling period. It is demonstrated that there is a significant improvement in terms of low-frequency harmonics and tracking error of the grid-side currents with the proposed method. In addition, the constraints imposed by the dead-time compensation are included in the optimization problem of the Space Vector Modulated Model Predictive Control algorithm. Finally, Hardware-in-the-loop results are presented to demonstrate the good performance of the proposed dead-time compensation technique.

This paper presents an improved sequential model predictive control (MPC) method for single phase photovoltaic (PV) applications. The T-type H-bridge five-level topology is selected in this paper. The proposed method achieves constant switching frequency operation of the inverter, which is advantageous over the variable switching frequency MPC methods. The proposed fixed switching frequency MPC (FSF-MPC) method eliminates the weighting factors, which represent critical element in applying classical MPC methods. Moreover, the proposed FSF-MPC method achieves low dv/dt values through using optimized switching sequences to evaluate the cost function. The simulation results of the proposed FSF-MPC method are provided and compared with variable switching frequency MPC method. The results show the fast tracking in both controllers. Whereas, fixed switching frequency and weighting factorless operation are obtained using the proposed FSF-MPC method. Additionally, lower total harmonic distortion (THD) in the output voltage is obtained using the proposed FSF-MPC method compared to classical MPC method.

Transformerless photovoltaic (PV) inverter topologies have proven superior performance over transformer-based inverter topologies. However, they face challenges due to their high leakage currents and voltage step-up requirements. There-fore, modified topologies and/or modulation techniques are essential to limit the current leakages to standard limits. The doubly grounded (DG) inverter topologies represent efficient solutions for totally limiting the leakage currents. This paper presents a finite control set (FCS) model predictive controller (MPC) method for DG step-up five-level PV inverter topology. The proposed FCS-MPC method can control the injected grid currents and multiple capacitors voltages simultaneously with high power quality output. The modelling and design of the step-up inverter topology and the FCS-MPC method are given in the paper. Simulation results with step-up DG five-level PV inverter topology are performed to validate the proposed FCS-MPC method. Moreover, the proposed FCS-MPC method has been validated using simulation results at the various operating conditions of PV inverters.

Leakage currents represent critical issues for the operation of conventional photovoltaic (PV) inverter topologies. Recently, the common grounded (CG) PV inverter topologies have presented superior performance over the conventional PV inverter topologies from the leakage currents elimination point of view. The multilevel CG-PV inverter topologies combine both features of the multilevel output voltage and the CG connection. Among the existing CG topologies, the switched capacitor types have found wide applications due to the lower cost, volume, and losses on the passive inductors. However, burdens have been risen for the control system to achieve multi-objectives, which includes controlling the output current of the inverter and the capacitor voltages. Therefore, this paper presents a Finite Control Set Model Predictive Control (FCS-MPC) method for five-level CG single-phase PV inverter topology. The FCS-MPC method presents reduced harmonic contents of the output currents and precise tracking to its reference value. The design guidelines of the FCS-MPC method and weighting factor adjustments are provided in this paper. Simulation results are presented in order to validate the effectiveness of the FCS-MPC method.

This paper describes a simple optimum carrier-based geometrical modulation for both NPC and T-Type inverters. The current that flows through the midpoint of the DC bus can be controlled in order to balance the capacitor voltages for a wide range of modulation indexes and power factors. This was accomplished by adding to the reference phase voltages a common-mode voltage vo,obtained by solving an optimization problem. In order to validate the good performance of the proposed modulation strategy, experimental results with Typhoon HIL are presented for NPC and T-Type inverters for different modulation indexes and power factors.

This paper proposes a Modulated Model Predictive Control (M2PC) algorithm for the three-phase Packed-U-Cells, PUC5, converter, that aims to minimize the output current error and balance the voltages of the DC bus capacitors. In order to do so, two cost functions are defined. The first one selects the sector in the SV diagram that minimizes the output current error, not taking into account the redundancies of the converter voltage vectors. After this, a second cost function for balancing the DC bus capacitor voltages is evaluated, considering only the voltage vectors of the chosen sector, including their redundancies. In this way, the proposed M2PC results in output voltages with fixed switching frequency, having a positive impact on the design of the output filter. Simulation results are shown, comparing the proposed M2PC and the standard variable frequency MPC strategies in terms of output current and voltage, internal capacitor voltage balancing and harmonic content.

Generally, space vector modulation strategies employ the Three Nearest Vectors (NTV) to the reference voltage vector in order to achieve low distortion on the output voltages. When it comes to multilevel converters, the modulation technique must deal with a large number of redundancies and sometimes it must also perform the balance of the capacitor voltages, depending on the converter topology. This paper proposes a space vector modulation strategy applied to Packed-U-Cell Converters (PUC). It is proposed a cost function to be minimized comprising two objectives: (i) adjacent switching vectors distance minimization (ii) desired currents in order to keep the capacitors charged. The proposed cost function minimization results in a optimized switching sequence and balanced capacitor voltages. Simulation results demonstrate the good performance of the proposed modulation strategy.

The IPC is a technique used for reducing structural loads in wind turbines like wind shear, tower shadow and wind turbulence by means of a control loop whose outputs are the resulting Tilt and Yaw Moments and the manipulated variables are the blade pitch angles. As the wind turbine model for structural analysis is highly nonlinear, with parametric uncertainties and variations, this paper proposes an IPC-based decentralized Robust Model Reference Adaptive Controller, RMRAC, aiming to address these challenges. The proposed IPC-RMRAC makes use of strain gauges to measure the blade root loads and controls each blade pitch angle independently. Simulations with 1.5 MW wind turbine using NREL FAST software were performed, showing the feasibility of the proposed algorithm. The results are discussed and compared with Collective Pitch Controller.

This paper extends the carrier-based discontinuous modulation for inverters with three parallel magnetically coupled legs. In order to simplify the analysis and implementation, the modulation is developed in the converter phase voltage framework. The core of the modulation is a state machine, that allows the distribution of the PWM pulses among the parallel legs, without increasing the number of commutations, when compared to conventional PD modulation methods. A detailed discrete-time model is given, aiming to design the controller for the circulating currents. Finally, simulation and experimental results are given, to validate the theoretical analysis and to demonstrate the feasibility of the proposed modulation technique.

This paper proposes an algorithm for the on-line definition of switching sequences for Space Vector Modulation (SVM) of the low voltage power cells of asymmetrical multilevel cascaded converters. Initially, an algorithm to define on-line the redundancies of the three nearest low voltage vectors is described in details. The first vector of the new sequence is chosen so as to minimize the switching losses between two consecutive reference samples. Then, the remaining vectors are organized within the switching period in a way such as to have the least number of commutations. As a result, minimum switching losses is achieved, even in the transition between sectors. Furthermore, the algorithm can also define sequences for any fault condition, avoiding the use of different look-up tables. Finally, experimental results are shown to validate the proposed algorithm.

This paper proposes a new and simple hybrid modulation strategy for asymmetrical cascaded multilevel converters (ACMCs) operating under fault conditions in the low-voltage-high-frequency (LVHF) cells. The theoretical analysis is explained through an example, an ACMC with one 1pu and one 2pu cell per phase. The modulation strategy integrates the benefits of both geometrical and space vector approaches. The last one is used to define the voltages for the high-voltage-low-frequency (HVLF) cells, such as to avoid the overmodulation of the LVHF cells. The carrier-based geometrical approach is employed for the LVHF cells to simplify the implementation of the algorithm, since the PWM signals are obtained via the comparison of modulating voltages to triangular carriers, with no need to define switching sequences for the low voltage vectors. As a result, the proposed algorithm is considerably simplified when compared to the space vector. Simulation and experimental results are given to validate the proposed method and to demonstrate its good performance.