Model Predictive Control (MPC) has become a widely adopted control technique for Multilevel Inverters due to its ability to manage multi-objective optimization problems under system constraints. However, a key challenge in MPC implementation lies in selecting appropriate weighting factors for the cost function, as fixed values often lead to suboptimal performance under dynamic operating conditions. Thus, this paper presents a lightweight auto-tuning method for the voltage weighting factor in Finite Control Set MPC (FCS-MPC), applied to a single-phase grid-connected 9-level Crossover Switches Cell inverter. The proposed approach employs low computational complexity machine learning models, Linear Regression and Support Vector Machine, trained offline on a minimal dataset comprising the DC link voltage and reference current. These models are embedded into the control loop to enable real-time adjustment of the voltage weighting factor. The presented comparative simulation results confirm the effectiveness of the proposed technique across a wide range of operating conditions. Compared to more complex AI-based solutions, this work contributes a simple yet effective ML-based tuning strategy that improves control performance with minimal computational overhead.

The increasing deployment of renewable energy systems, electric vehicles, and high-voltage direct current transmission infrastructures has intensified interest in Modular Multilevel Converters (MMCs), which offer superior scalability, waveform quality, and fault tolerance. However, MMC control remains challenging due to its nonlinear dynamics and multi-objective requirements, including output current tracking, capacitor-voltage balancing, and circulating current suppression. This paper presents a model-free control strategy based on Deep Reinforcement Learning (DRL), employing the Proximal Policy Optimization algorithm to achieve these control objectives in a 3-level single-phase MMC. The proposed DRL-based controller learns an optimal switching policy directly from interaction data, eliminating the need for an accurate system model or manual tuning. Simulation results from MATLAB/Simulink confirm that the trained agent achieves low total harmonic distortion, maintains capacitor voltages around the desired values, and minimizes the circulating current, while demonstrating robustness under load variations and dynamic transients. These findings highlight the effectiveness of the proposed DRL approach as a scalable and adaptive solution for complex multilevel inverter control problems.

This paper proposes a self adaptive physics informed neural network (SAPINN) model to predict the degree of polymerization (DP) of oil-impregnated paper insulation to quantify the level of degradation and the remaining useful lifetime. The prediction is performed based on historical DP values and the corresponding prediction time step, which are used as input data points to the proposed model. The DP mathematical model is used to constrain the training phase of the AI-model through a weighted sum loss function. The weights of this loss function are adjusted for each epoch through a self-adaptive weighting method to determine the relative importance of the data component and the mathematical model throughout the training by defining these weights as trainable parameters. The trained model is then tested using different datasets which are not part of the training phase. The training and testing datasets are generated synthetically through an algorithm that considers the deviation from the ideal DP degradation curve and incorporates actual measurement noise. The performance of the proposed SAPINN is compared to the baseline PINN and NN (in the absence of physics) to highlight the importance of embedding the mathematical model and the self adaptation algorithm, and theses experiments demonstrate that SAPINN significantly enhances the DP prediction.

In this paper, a Reinforcement Learning controller (RLC) is designed and implemented on a 5-level Packed U-Cell (PUC5) grid-connected inverter to control the injected current flowing into the electric network. The RL agent is trained using a Proportional-Integral (PI) reward function to optimize its control strategy. Moreover, the voltage balancing of the auxiliary capacitor in PUC5 is separated from the RL controller and integrated into the switching algorithm to reduce the training burden. This modification reduces the observation inputs required for RL training, significantly shorten the training time. Simulation studies conducted in Matlab/Simulink evaluate the performance of the proposed RL controller, demonstrating robust dynamic response and accurate tracking of reference signals across different operational conditions.

Packed-U-Cell (PUC) is a single DC source multi-level inverter that can be used in many applications such as grid-connected photovoltaic (PV) systems. In this application, the total harmonic distortion (THD) of the generated current signal should be minimized while operating at the unity power factor (maximum active power transfer). These objectives can be achieved by regulating the auxiliary capacitor voltage around its reference value while tracking the reference current signal that varies with the PV maximum power point (MPP). Thus, this paper proposes a reinforcement learning (RL) based controller that satisfies the aforementioned control objectives using the actor-critic RL architecture and the proximal policy optimization (PPO) learning algorithm. The designed RL-based controller is applied on a single-phase 5-level PUC inverter. The proposed design is validated through simulations where the obtained control policy resulted in a maximum absolute voltage error of 1.9 V and a THD value of 2% and 4.5% for reference current peak values of 8.8A and 4.2A, respectively. Furthermore, the proposed RL-based controller shows high robustness to parameter variations (different capacitor and inductor sizes).

In this study, a finite control set-sliding mode controller (FCS-SMC) is applied to a grid-connected 9-level Crossover Switches Cell (CSC9) inverter. The proposed control technique is model-independent, as it selects the control actions based on the current state variables' errors rather than the past values. The control algorithm optimizes the control inputs to fulfill the reaching requirements of two designed cost functions that were derived based on the sliding mode (SM) theory. Moreover, a small permissible capacitor voltage error (hysteresis width h) is introduced to add an extra degree of freedom to the system, which allows the reduction of the average switching frequency without compromising the design requirements. The main advantages of the proposed method lie in its simplicity, absence of gain adjustment requirements, and straightforward design process. Simulation results are presented to demonstrate the effectiveness of the proposed SMC in achieving excellent dynamic performance and robustness against disturbances while controlling the capacitor voltage and grid current.

This study proposes the application of a Lyapunov-based Model Predictive Control (L-MPC) approach to a 9-level Crossover Switches Cell (CSC9) converter operating in grid connection mode. The proposed method utilizes the structure of the classical finite-control-set MPC (FCS-MPC) technique while integrating a cost function that requires no tuning. By deriving the cost function based on Lyapunov theory, the system stability is ensured. Notably, the suggested approach offers several advantages over traditional MPC controllers. Firstly, it eliminates the need for gain tuning, thereby simplifying the implementation process. Secondly, the proposed controller prioritizes stability as a key design aspect. The presented simulation results prove that the proposed controller effectively regulates the voltage of the DC capacitor around its desired value and feed a smooth sinusoidal current to the grid with low total harmonic distortion (THD) while operating at a unity power factor.

In this paper, an optimized auto-tuning of Finite Control Set-Model Predictive Control (FCS-MPC) is proposed for a 9-level Crossover Switches Cell (CSC9) inverter. The system under study, a grid-connected single-phase CSC9 inverter-based PV system, is designed to extract maximum power from the PV panels and feed it to the grid with unity power factor and low current total harmonic distortion (THD). These objectives are achieved while regulating the CSC capacitor’s voltage at its reference value to maintain the 9 voltage levels at the inverter output terminals. In the cost function design, the minimization of the switching transitions along with the errors on the capacitor voltage and grid current (consequently current THD) were selected as control objectives, where the higher priority was given to the latter. The objective of reducing the switching events is assumed to have the least priority while the weighting factor on the capacitor voltage is dynamic and its value is determined by a pre-defined polynomial. Moreover, the optimization of the cost function is performed over two-step prediction horizon. The performance of the proposed control design (current THD enhancement and switching transitions reduction) is compared in simulation with the fixed weighting factor and one-step horizon without switching transitions constraint FCS-MPC case scenario.

The present study investigates the effect of the interface defects for ultrathin Cu (In 1-x Ga x ) Se 2 solar cells using Silvaco TCAD. The initial simulation study is carried out with the reference cell configuration in which the PV parameters fit and match the fabricated cell characteristics. Our goal is to investigate the temperature dependence of the performance of u-CIGS solar cells in the temperature range of 10–70 °C and the loss mechanism caused by interface trap density (D it ) in different cell pitch sizes on cell performance. D it defines the amount of carrier’s traps at CIGS/Al 2 O 3 interface to recombine with photogenerated carriers. The recombination mechanisms are found to play a crucial role in cell performance degradation. Further simulations quantify significant improvements in J sc and V oc for different cell pitch sizes at a fixed opening width in the Al 2 O 3 layer. Consequently, the optimum efficiency has been pointed out at a cell pitch size of 1.5 µm. MgF 2 has been used as an ARC layer to boost the efficiency up to 14 %. The results from these simulations provide insights for passivated u-CIGS solar cell optimization.

In this paper, a finite-control-set model predictive controller (FCS-MPC) is designed for a grid-connected single-phase crossover switches cell (CSC) multilevel inverter (MLI). The CSC inverter is a modified 3-cell packed-U-cells (PUC) MLI by adding two crossover switches between the two DC-link and the flying capacitor. This modification allows the CSC to generate an output voltage with a peak value greater than the value of the DC source voltage (boost capability). The proposed FCS-MPC is designed, by selecting the appropriate weighting factor values for different design parameters, to inject current to the grid at unity power factor and low total harmonic distortion (THD) while regulating the capacitor voltage at the reference level. The proposed FCS-MPC is validated in simulation where the presented results show a high dynamic performance and capability of dealing with a multi-objective control problem at different operating conditions.