High-power modular multilevel converters: Modeling, modulation and control

The Modular Multilevel Converter (MMC) is a consolidated attractive technology taking great importance in medium / high power applications compared to other topologies of multilevel converters. One of the main lines of research is on voltage modulation techniques and its influence on the converter performance. Specifically for a relatively high number of submodules (SMs) that may depend on the specific application type. In this paper, a brief overview of the MMC basic operation is presented and the modulation techniques based on switching frequency as the main parameter are studied. Also, three different modulation techniques are studied to later be performed in Matlab/Simulink, in simulation is considering two cases of MMC systems with 5 and 20 SMs/arm, in order to comparing the impact of modulation technique on MMC performance.

Model predictive control (MPC) has emerged as a promising approach to control a modular multilevel converter (MMC). With the help of a cost function, the control objectives of an MMC are achieved easily by using an MPC approach. However, the MPC has several technical challenges and issues including the need of accurate system models, computational complexity, and variable switching frequency operation and weighting factor selection, when it comes to the control of an MMC. In the past few years, several research studies are conducted to address some of the challenges and issues in an MPC and developed several model predictive algorithms for an MMC. In this paper, the importance of each challenge and its impact on the system performance is discussed. Also, the MMC mathematical models used in the implementation of MPC are presented. Furthermore, some of the popular MPC algorithms are discussed briefly, and their features and performance are highlighted through case studies. Finally, summary and future trends of MPC for an MMC are presented.

Modulation strategy plays an important role in the good performance of modular multilevel converter (MMC). Since MMC has a special structure as well as a large number of submodules (SMs), it is almost impossible to apply the conventional multilevel space vector modulation (SVM) algorithm in it. In this study, a simplified SVM implementation method is proposed by grouping SMs. With the presented method, the high‐level SVM process for MMC can be easily done by implementing multiple two‐level or three‐level SVM algorithm, while the complicated offline or online efforts/tasks about mapping switching states or sequences in the conventional multilevel SVM are not necessary any more. In order to improve the harmonic characteristics, the sampling time of different subunits (SUs), each of which is formed by grouping a few SMs, is staggered evenly, which helps to achieve a higher equivalent switching frequency and less low‐order harmonics. Further, the capacitor voltage balance of SMs is improved by rotation strategies between SUs or inside each SU even without any capacitor voltage detecting or sorting. Since the presented method is independent of the number of SMs, it can be very easily expanded into a high‐level MMC. These propositions are verified by experiment.

Submodule (SM) failure is one of the key factors leading to instability in modular multilevel converter (MMC) systems. To address this challenge and enhance system reliability, redundant configurations have become the core solution. However, existing redundancy strategies cannot simultaneously balance the transient repair speed and steady‐state output accuracy. Hence, a novel fusion redundant control scheme that combines cold‐standby and hot‐standby redundancy strategies is proposed to overcome the challenge of optimizing both transient repair speed and steady‐state output accuracy. First, an MMC structure is proposed, which is equipped with both cold‐standby SMs and hot‐standby SMs and realizes the sharing of cold standby SMs. Then, the casting and switching strategies of the SMs are designed based on this structure. Finally, a bridge arm reference voltage update module is designed to avoid the voltage offset caused by the coordination of the cold‐standby module and the hot standby module. Simulation and experimental results confirm that the proposed method effectively addresses both transient and steady‐state performance issues, facilitates rapid repairs, and ensures precise and reliable operation of the MMC system after a fault event.

One of the main challenges in the control of modular multilevel converters (MMC) is reducing circulating currents and keeping sub-modules capacitors voltage regulated. This paper introduces a method which controls the voltage and current simultaneously by controlling the reference values using the equations of the MMC and genetic optimization algorithm. The proposed method controls the converter by adding the second harmonic component of the reference waveform, then calculating the working condition to reach the minimum circulating current and voltage ripples. The method is verified using MATLAB Simulink.

Long-length horizons dynamic matrix predictive control for a MMC inverter

Model predictive control (MPC) as a nonlinear control method, is being studied for a wide variety of power electronics applications targeting various power ranges. The fast dynamic response, robustness under different load conditions, multi-objective control, and simplicity of implementation are the main features of MPC. However, the implementation of finite control set MPC (FCS-MPC) or direct MPC in its basic form for multilevel converters (MLCs) demonstrates the inability to generate the staircase multilevel output voltage waveform in a voltage sourced inverter (VSI) application. Adding a reference evaluation block to the basic FCS-MPC method can significantly improve its multilevel voltage generation capability. In this paper, an enhanced model predictive nearest level control (EMP-NLC) is proposed and implemented on a 5-Level flying capacitor multilevel converter (FCMC). The proposed method is simulated under different load conditions, while a comparison against existing MPC techniques is also presented. Experimental results verified the efficacy of the proposed controller.

Modular multi-level converter (MMC) is one of the promising voltage source converter (VSC) topologies in recent years due to its modularity and scalability. Generally, the main technical challenge of different VSC topologies is the voltage balancing of sub-modules capacitors. Conventional sensor-based balancing techniques require a large number of voltage sensors which increases system cost and complexity. In this paper, a new balancing technique with a reduced number of voltage sensors is proposed. In the proposed technique, only one sensor per arm is needed (arm voltage control). The basic role of the proposed balancing technique is controlling the arm voltage to follow its instantaneous voltage reference by selecting appropriate combination from the available states of arm sub-modules. The proposed technique is tested during normal as well as abnormal operating conditions. A simulation model has been built to validate the proposed balancing technique.

In this paper, we consider an optimization problem solving for model predictive control (MPC) of a modular multilevel converter (MMC). An MMC consists of a large number of submodules. The objective of the MPC is to determine the best switching sequences for the submodules in the MMC to track the phase current references for T time horizons. The MPC is formulated as a nonlinear mixed-integer programming (MIP) problem with the on/off status of submodules as binary decision variables and MMC dynamic states such as phase currents, circulating currents and submodule capacitor voltages as continuous decision variables. With a large number of submodules and a large number of time horizons, the dimension of the nonlinear MIP problem becomes difficult to handle. Our contribution is to formulate this problem and solve this problem using Bender's decomposition. An example 5-level single-phase MMC is demonstrated for the proposed algorithm.

Small‐signal modelling of modular multilevel converters (MMC) has drawn lots of attention recently. Existing research mainly studies MMC terminal impedances by injecting corresponding AC‐side and DC‐side external small‐signal perturbations. This paper focuses on the MMC internal small‐signal perturbations, that is, perturbations in control signal and their effect. There are two kinds, that is, differential‐mode and common‐mode, of internal small‐signal perturbations, which influence MMC external and internal state variables, respectively. Then, the existing MMC small‐signal model is improved. With its help, accurate MMC control‐signal to state‐variables transfer functions are established, which can aid precise MMC controller analysis and performance optimization. In this paper, an analysis of MMC phase capacitor voltage regulation control is demonstrated as an example. The stability can be precisely ascertained based on the obtained transfer function. The effect of parameter variation of the proportional‐integral controller is also studied. Moreover, an additional compensator is introduced in the existing control loop to attenuate resonance peaks in the transfer function, which mitigates the large voltage overshoot and makes the transient state much smoother. Simulation and experimental results support and well confirm the analysis of this paper.

Modular Multilevel Converters (MMC) are known for their high modularity and good quality output power. The problems of circulating current and capacitor voltage balancing for the MMC have been widely studied in available literature but little work is done towards grid current control with fast dynamic response. Conventional current controllers use PI or PR controllers which are slow and thus degrade transient performance of the MMC. Recently reported Model Predictive Control (MPC) methods for MMC aim to control the grid current but MPC suffers from computational burden and difficulties in adjusting of weights. Space Vector based Hysteresis Current Controllers (SCHCC) have been well established for drive applications. In this work, an SVHCC has been developed for a grid connected MMC system. Simulation studies show fast current control for cases of sudden change of active power reference and current reference. The controller exhibits quick tracking of the current reference in all cases. The proposed method has been discussed for a MMC system with three submodules, but it may be extended to any number of submodules and can be easily implemented in a digital controller with less computational effort.

Due to the ability to control several parameters by the cost function and the high dynamic response as well as direct modeling, the model predictive control (MPC) is among the best methods for controlling a modular multilevel converter (MMC). In conventional MPC, the optimal value of the cost function is obtained after calculating all switching states. If the number of sub-modules (SMs) increases, the computational volume also increases exponentially, which makes this method impossible to implement. In this paper, an improved indirect model predictive control (IIMPC) is presented to control the parameters of MMC, such as circulating and load currents and capacitor voltage. Moreover, this method can make the computational volume independent from the number of SMs. On the other hand, to control the voltages of the capacitors, a sorting algorithm has been used. The superiority and privilege of the proposed IIMPC are verified by simulation results of a five-level single-phase MMC with four SMs in each arm.

The internal dynamics governing the modular multilevel converter can be described by considering the total capacitor's voltages of each arm and the circulating currents. In this study, dynamics of the total capacitor voltage of the modular multilevel converter (MMC) is analyzed. Depending on the internal control approach adopted which ensure a symmetrical energy distribution in the arms of the converter, the capacitor's voltage ripple is evaluated, highlighting effects of both the fundamental and the double frequency. Interestingly, it shows a reduction of the overall requirement of the installed energy. Simulation results are presented to verify the accuracy of the analysis.

ABSTRACTThe modular multilevel converter (MMC) has attracted significant attention in the fields of high and medium‐voltage applications in recent years. Traditional indirect finite control set model predictive control (IFCS‐MPC) strategies for MMC require voltage measurements from each submodule (SM) for the voltage control loop. As a result, a considerable number of sensors is required to achieve energy balancing, leading to increased circuit complexity and higher hardware costs. This paper proposes an IFCS‐MPC method for MMC with a reduced number of sensors approach. In the approach, an SM capacitor voltage observer is designed based on the discrete‐time model of capacitor voltage, utilizing the measured arm current and SM switching state. Only one SM voltage sensor is needed per arm, which is used to correct the observed voltage errors caused by the discrete‐time model and the accuracy of the parameters. Subsequently, the observed values of the SM capacitor voltages are substituted for the measured values in the voltage sorting algorithm of IFCS‐MPC to reduce voltage sensors. The simulation and experiment results are carried out to demonstrate the effectiveness of the proposed control system and the feasibility of reducing the number of sensors.

Modular multilevel converter (MMC) has been widely used in high-voltage direct current (HVDC) transmission system due to its advantages of modularity, low switching frequency and excellent output voltage waveforms. Proper operation of MMC is based on capacitor voltage balancing. Conventional MMC capacitor voltage balancing control needs a large number of voltage sensors and sorting algorithm which increases computing burden on the processor. In this paper, a capacitor voltage self-balancing MMC with DC fault ride-through capability is proposed. Capacitor voltage balancing control of this proposed MMC is significantly simple due to its self-balancing auxiliary circuit. Sorting algorithm is not needed, each phase leg only needs one voltage sensor for top SM. So control complexity and computing burden are reduced greatly. Furthermore, DC fault ride-through capability is also reserved. Simulation results validates capacitor voltage of this new MMC topology can be well balanced and DC fault ride-through capability is effective.