Model-predictive control (MPC) has emerged as a promising control method in power electronics, particularly for multiobjective control problems such as multilevel inverter (MLI) applications. Over the past two decades, improving the performance of MPC and tackling its technical challenges, such as computational load, modeling accuracy, cost function design, and weighting factor selection, have attracted great interest in power electronics. This article aims to discuss the current state of MPC strategies for MLI applications, describing the significance of each challenge with the reported effective solutions. Through this review, the MPC methods are categorized into two groups: direct MPC (without modulator) and indirect MPC (with modulator). The recent advances of each category are presented and analyzed, focusing on direct MPC as the most applied method for MLI topologies. In addition, some of the important concepts are experimentally validated through a case study and compared under the same operating conditions to evaluate the performance and highlight their features. Finally, the future trends of MPC for MLI applications are discussed based on the current state and reported developments.

Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DL-based FDD for PV systems and provide guidelines for future research in the field.

Having a reduced number of switches and isolated DC sources yet generating a higher number of voltage levels has been always the challenge when selecting the appropriate Multilevel Inverter (MLI) topology. Nowadays, Single-DC-Source Multilevel Inverter (SDCS-MLI) topologies are being considered as more suitable for many power system applications such as Renewable Energy (RE) conversion systems and electrified transportations compared to the Multiple-DC-Source MLIs (MDCS-MLIs). Moreover, increasing the power rating and minimizing the switching frequency while maintaining reasonable power quality using a SDCS-MLI is an important requirement and a persistent challenge for the industry. Thus, this paper presents a general review on available SDCS-MLI topologies and future trends. Existing solutions are discussed and analyzed based on their topologies, number of output voltage levels, number of active/passive components, advantages/limitations, maturity, and industrial applications. Furthermore, recommendations for future research and development are suggested in this paper

This paper presents a finite-control-set model predictive control (FCS-MPC) for grid-tied packed U cells (PUC) multilevel inverter (MLI). The system under study consists of a single-phase 3-cells PUC inverter connected to the grid through filtering inductor. The proposed competitive topology allows the generation of 7-level output voltage with reduction of passive and active components compared to the conventional MLIs. The aim of the proposed FCS-MPC technique is to achieve, under various operating conditions, grid-tied current injection with unity power factor and low total harmonic distortion while balancing the capacitor voltage. Parameters' sensitivity analysis was also conducted. The study is conducted on a low-power case study single-phase 3-cells PUC inverter and with possible extension to higher number of cells. Theoretical analysis, simulation, and experimental results are presented and compared.

Modular multilevel converter (MMC) is a promising new topology for high-voltage applications. The MMC is made of several identical submodules. For proper operation, each submodule can be considered as a controlled voltage source where capacitor's voltage should be maintained at a certain level. Besides, the minimization of the circulating current, which does not flow to the load, is crucial for achieving stable and efficient operation of the MMC. The interrelations among the load current, circulating current, and capacitor voltages complicate the MMC control. This paper aims to achieve stable and balanced voltage and current control with reduced circulating current in various operating conditions. The proposed control uses weighted model predictive control based on a normalized cost function to select the inverter switching patterns, which control the load current, while minimizing voltage fluctuation and circulating current. The weighting factors were selected based on minimizing the load-current total harmonic distortion (THD) and circulating current. The analysis is conducted on a low-power case study of single-phase four-cells MMC with possible extension to higher number of cells. The low-power three-level prototype is designed and built to validate this proposed method. Theoretical analysis, simulation, and experimental results are presented and compared. Parameter sensitivity analysis was also conducted. They all confirm the effectiveness of the proposed control method.