The modular multilevel converter is a good alternative for high-power, medium-voltage drive applications due to its modularity and scalability. However, the control is complex and typically involves several highly coupled circulating current feedback loops implemented using single-input single-output design tools. In addition, each circulating current has many different frequency components and electrical sequences to increase the degrees of freedom in the controller. In this work, the use of continuous control set model predictive control is proposed in order to include cross-coupling and interactions between the state variables and to consider the system constraints, such as maximum current and maximum output voltage. The controller is intended for drive applications and is designed to operate with good dynamic performance over the entire speed range of the machine. The control methodology proposed is experimentally validated using an 18-cell MMC prototype driving a cage machine. Additional experimental tests are performed using PLECS-RT HIL platforms.

Implementing control schemes for modular multilevel converters (M2Cs) involves both a cyber and a physical level, leading to a cyber-physical system (CPS). At the cyber level, a communication network enables the data exchange between sensors, control platforms, and monitoring systems. Meanwhile, at the physical level, the semiconductor devices that comprise the M2C are switched on / off by the control system. In this context, almost all published works in this research area assume that the CPS always reports correct information. However, this may not be the case when the M2C is affected by cyberattacks, such as the one named false data injection attack (FDIA), where the data seen by the control system is corrupted through illegitimate data intrusion into the CPS. To deal with this situation, FDIA detectors for the M2C are recently starting to be studied, where the goal is to detect and mitigate the attacks and the attacked submodules. This article proposes a reinforcement learning (RL)-based method to uncover the deficiencies of existing FDIAs detectors used for M2C applications. The proposed method auto-generates complex attack sequences able to bypass FDIA detectors. Therefore, it points out the weaknesses of current detectors. This valuable information can be used later to improve the performance of the detectors, establishing more reliable cybersecurity solutions for M2Cs. The RL environment is developed in MATLAB/Simulink augmented by PLECS/blockset, and it is made available to researchers on a website to motivate future research efforts in this area. Hardware-in-the-loop (HIL) studies verify the proposal's effectiveness.

This paper presents a new enhanced single-stage two-step Continuous Control Set Model Predictive Control (CCS-MPC) algorithm, for Modular Multilevel Converters (MMCs) operating with variable output frequency at the AC port. The proposed two-step CCS-MPC allows the implementation of feed-forward compensation of the one-step ahead perturbations at the AC side, using an algorithm based on the Moore-Penrose pseudoinverse matrix. Limitations of the current and voltages in the MMC clusters are implemented to avoid overmodulation and overcurrents, being the solution of the constrained CCS-MPC online solved using the active-set method. Without losing generality, the experimental validation of the single-stage CCS-MPC is performed using an 18-cell MMC prototype, driving a 7.5 kW cage induction machine operating in a wide speed range. A dSPACE MicroLabBox platform is used to control the MMC-based drive by implementing the CCS-MPC algorithm, the modulation stage and the active-set method. It is experimentally demonstrated that the compensation of the low-frequency perturbations allows a considerable reduction in the required circulating currents at the low-frequency operation of the drive. Reduction in the capacitor voltage ripple, improved efficiency, and a smaller Total Harmonic Distortion (THD) in the output currents are also achieved with the proposed single-stage two-step CCS-MPC.

This article discusses a continuous control set model predictive control system of a hybrid modular multilevel converter, controlling a direct drive permanent magnet synchronous generator, for variable-speed wind energy applications. The hybrid converter enables operation at a reduced dc-link voltage without the need for circulating currents, even during low-frequency operation in the ac port. To further reduce cluster currents, the capacitor voltages are allowed to oscillate inside predefined voltage limits. The control system is based on a single-step predictive control algorithm, whose outputs are the dc-link and common-mode voltages required to balance the converter energy. Furthermore, it is demonstrated in this work that the reference for the common-mode voltage, obtained using an algorithm based on the Moore–Penrose pseudoinverse matrix, is a third harmonic waveform, which can be used to balance the capacitor voltages, as well as to increase the modulation index. The proposed control system is validated using a hardware-in-the-loop platform and an experimental system consisting of a 5-kW back-to-back hybrid modular multilevel converter, controlled using dSPACE MicroLabBox platforms.

The modular multilevel converter (MMC) is a prominent solution for medium- to high-voltage and high-power conversion applications. Recently, distributed control strategies have been proposed to make this converter modular in terms of software and control hardware. In this control architecture, high-level control tasks are performed by a central controller (CC), whereas low-level control tasks are achieved by local controllers (LCs) placed on the MMC submodules. The CC and LCs use a cyber-physical system (CPS) to share all the necessary information to execute their respective control schemes. In this context, the CPS is vulnerable to cyberattacks, such as the false data injection attack (FDIA), where the data seen by the controllers are corrupted through illegitimate data intrusion. This cyberattack may hinder the MMC performance, producing suboptimal, or even unstable operations. Even more, diverse FDIAs can be generated using artificial intelligence methods to deceive FDIA detectors. This article proposes an FDIA detector based on the reinforcement learning (RL) technique to detect sophisticated FDIAs targeting the MMC control system. The performance of the proposed RL-based FDIA detector is verified via hardware-in-the-loop studies, showing its effectiveness in detecting sophisticated attack sequences affecting the MMC control system.

For relatively large grid-connected power converters, good low voltage ride through (LVRT) capability is mandatory. LVRT operation is typically achieved using control systems based on several resonant or proportional integral (PI) regulators, which are designed using simplified models that do not consider all the cross-couplings and interactions between the currents and voltages of different electrical sequences produced during a grid fault. As an alternative to conventional regulators, this article presents a novel continuous control set model predictive control (CCS-MPC) strategy for grid-connected power converters operating under LVRT conditions. The proposed controller achieves zero steady-state error in tracking sinusoidal current references through an augmented state-space model that incorporates resonant poles, thereby addressing a fundamental limitation of conventional MPC implementations. Some of the advantages and innovations of the proposed control methodology include: 1) a constrained optimization formulation with polytope-based current limits for fault scenarios; 2) a discussion of the discretization methodology required to ensure numerical stability of the predictive control system with embedded resonant poles; and 3) an analytical tuning methodology linking cost function weights to closed-loop dynamics. The controller’s performance is validated under both symmetrical and asymmetrical voltage dip scenarios through comprehensive simulations and experimental tests on a 2 kW prototype.