This paper presents a robust control strategy to address the frequency regulation challenges in low-inertia microgrids (MGs) with high penetration of renewable energy sources (RESs). A novel PID-based Virtual Rotor Control with Integral feedback (PID-VRC/I) is proposed to emulate virtual inertia using Electric Vehicles (EVs) and to mitigate the adverse effects of measurement delays caused by the phase-locked loop (PLL). To ensure reliable frequency support during periods when EVs are unavailable, a coordinated demand response (DR) mechanism is integrated as a backup control layer. The DR framework considers flexible loads such as pumps, chillers, compressors, and Uninterruptible Power Supply (UPS) systems, all controlled using a conventional VRC approach while taking into account their communication and response time delays. A modified zebra optimization algorithm (MZOA) is employed to optimize the proposed controller gains. The effectiveness of the proposed control framework is validated through two scenarios using hardware-in-the-loop (HIL) testing on the Real-Time Digital Simulator (RTDS) platform. Results demonstrate that the proposed PID-VRC/I with DR strategy significantly outperforms conventional VRC configurations by reducing frequency deviations, enhancing dynamic response, and minimizing stress on EV systems.

This paper presents a simple yet robust dq-frame current control strategy for a single-phase 5-level Packed U-Cell (PUC5) inverter, targeting efficient and reliable integration of renewable energy (RE) sources into the grid. The proposed approach exploits the inherent self-balancing characteristics of the PUC5 topology, thereby eliminating the need for capacitor voltage sensors or active balancing circuits. This leads to a simplified hardware structure and reduced overall system complexity and cost. A conventional PI-based dq controller is employed to regulate the injected grid current, ensuring decoupled control of active and reactive powers. Extensive simulations in MATLAB/Simulink demonstrate that the system maintains a unity power factor and achieves low total harmonic distortion (THD) using a basic L-type filter. Moreover, one of the key outcomes is the natural and stable self-balancing of the flying capacitor voltage, which consistently maintains its voltage around half the DC link voltage with low ripples, even under dynamic changes in current reference and DC input voltage. These results confirm the PUC5-controller solution’s reliability and suitability (compelling alternative to more complex multilevel converter architectures) for grid-connected applications.

In this paper, we propose a hybrid-state feedback control strategy for a DC-DC interleaved synchronous buck converter within the framework of hybrid dynamical systems theory. The control objectives—fast convergence and high efficiency—are formulated as a stabilization problem of an optimal limit cycle. To tackle this, we develop a hybrid automaton-based approach comprising two key components: i) a transient-state controller that guarantees finite-time balancing of the load current between converter phases, and ii) a steady-state controller that ensures local asymptotic stability of the desired limit cycle. The proposed method is demonstrated on a two-phase interleaved buck converter, although the design is readily extendable to n-phase converters and adaptable to other power converter architectures. Simulation results validate the theoretical developments and illustrate the robustness of the hybrid controller under load disturbances, input voltage variations, and parameter uncertainties.

The expanding integration of renewable energy (RE) sources into modern power introduces operational challenges such as frequency instability. This study focuses on Load Frequency Control (LFC) in the context of PV power integration. A Newton–Raphson-Based Optimizer (NRBO) is employed to tune the Proportional–Integral–Derivative (PID) controller parameters for a two-area interconnected power system, with performance compared against the Teach–Learning-Based Optimization (TLBO) method. Furthermore, the study evaluates the performance of a novel cascaded PD–P–PID controller relative to a conventional PID approach. Simulation results reveal notable improvements, including reductions in peak overshoot, undershoot, settling time, and oscillatory behavior. The influence of PV power integration into Area 1 on system frequency and tie-line power is also analyzed. To counteract frequency deviations arising from PV output variability, the paper proposes incorporating an Electric Vehicle (EV) system as an auxiliary frequency regulation resource.

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 presents an innovative control strategy to enhance the stability of interconnected Microgrids (MGs) with low inertia and high penetration levels of Renewable Energies (REs). The proposed control strategy encompasses a new virtual droop control mechanism that emulates the primary control of synchronous generators for enhanced system stability. Additionally, a weighted Proportional-Integral (PI) controller is used to mitigate the adverse effects of measurement delays caused by Phase-Locked Loop (PLL) dynamics. Furthermore, a feedback integral loop is introduced to improve the efficiency and lifespan of Vanadium Redox Flow Batteries (VRFBs) enabling swift and precise power delivery while reducing steadystate errors. Finally, a new fractional-order virtual inertia control (VIC) is introduced to leverage the fractional derivatives and enhance the system’s frequency response. The presented simulation results demonstrate the effectiveness of the proposed control approach in improving the frequency response and power exchange dynamics across interconnected MGs under various operating scenarios.

This paper addresses the limit cycle stabilization problem of multilevel Flying Capacitors Inverters (FCI). Firstly, the hybrid modeling and analysis of the converter dynamics are discussed for control design purposes. Then, hybrid automaton-based controller is investigated to address the fast-transient and high-efficiency compromise. The latter is translated as an optimal limit cycle stabilization problem. Finite-time stability of the desired limit cycle under the proposed hybrid controller is proved analytically. The presented simulation results confirm the theoretical claims and show the robustness of the controlled converter despite load disturbances and fast input variations.

This work presents an improved Perovskite/u-CIGS tandem solar cell architecture, exhibiting significantly enhanced conversion efficiency and higher reliability compared to existing solutions in the literature. At the bottom cell level, traps (Dit) were identified at rear u-CIGS/Al2O3interface as a key factor degrading conversion efficiency by promoting carrier recombination. Mitigating this mechanism is essential for improving the stability and performance of solar cells. Incorporating a negative fixed charge density (Qf) in the Al2O3layer offers a promising solution, providing effective field-effect passivation to minimize minority carrier recombination at the rear surface. The proposed tandem device achieves an impressive efficiency of 23.28% using a cell pitch of 1.5 μm and silver as the back specular contact.

Speed sensor faults are very common in electric vehicle (EV) applications, often disrupting system performance due to the reliance on speed sensor data by the electric drives. Consequently, fault-tolerant control approaches require robust observer structures capable of functioning effectively during faults, swiftly identifying and reconfiguring failures. Thus, this paper presents a novel fault-tolerant control design leveraging a synergetic observer (SO) to substitute the speed sensor in the event of fault. Comparative analysis with a super-twisting sliding mode observer (ST-SMO) highlights the proposed observer's superior performance in terms of tracking accuracy and ripple reduction. The presented simulation results demonstrate promising performance of the proposed observer for fault-tolerant control in EV applications.

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.

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage.

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).

This paper proposes an effective control approach based on fractional order sliding mode control (FOSMC) for autonomous distributed generation systems (ADGS). The design of the controller is based on the mathematical model of the system formulated in the abc reference frame and by including a static equation to reduce the number of control variables for the system to become square and optimize the control signals. Stability and robustness analyses are performed using Lyapunov's theory to demonstrate the robustness of the proposed controller against model uncertainties, while ensuring low total harmonic distortion (THD) even in the presence of non- linear loads. The presented simulation results obtained using MATLAB/Simulink software confirm the high performance and superiority of the proposed controller against other conventional controllers in regulating the output voltage of the ADGS for different types of load (balanced, non-linear).

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.

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.

Accurate Short-Term Load Forecasting (STLF) has been considered a topic of extreme importance for efficient energy management, reliable energy transactions, and economic operation dispatch in smart grids. However, the continuous instability of the load demand essentially due to the high volatility of weather conditions and customers' demand behavior dramatically affects the STLF accuracy. In order to overcome this problem, five effective Deep Learning (DL) techniques are proposed for multivariate time series STLF based on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and stacked Auto-Encoder (AE). These DL based techniques are consolidated to build stacked Bidirectional GRU (BiGRU), Convolutional LSTM (ConvLSTM), stacked Bidirectional LSTM-AE (BiLSTM-AE), hybrid CNN-LSTM-AE (CNN-LSTM), and LSTM-AE (LSTM-AE) techniques. Simulation studies are conducted to demonstrate the performance superiority of BiLSTM-AE compared to the other DL models. The main contributions of this paper include 1) integrating a variety of deep neural networks for STLF; 2) employing time series as a benchmark to compare between heterogeneous DL architectures; 3) conducting the analyses on real data set.

This paper presents a novel state feedback controller design for a 5-level Packed U-cell (PUC5) inverter based on its average model. The state feedback is designed using a super-twisting sliding mode control (ST-SMC) and a PWM technique. The proposed super-twisting controller (STC) is used to regulate the capacitor voltage at the desired value in order to generate a 5-level output voltage waveform while feeding a smooth current to the load with low harmonics. A simulation and experimental comparative study between the proposed control technique and the conventional SMC and proportional-integral (PI) control techniques is presented. The presented results prove the higher performance of the proposed controller in terms of dynamic performances and power quality.

In this work, a three-phase two-leg T-type active power filter topology is investigated when a three-level hysteresis band current modulation technique is applied. The three-phase two-leg T-type configuration which utilizes eight switching devices together split DC link capacitor banks is able to improve the reliability of the system as well as effectively reduces the power losses. The compensation of the current harmonics at each phase are achieved successfully with the control of phase-a and phase-b. Using this technique, the phase-c is directly connected to the midpoint of the split capacitors, which eliminates the necessity of additional control on the phase-c. The performance of the proposed topology and control method is demonstrated through the simulation results.

In this paper, a novel and simple controller for seven-level packed U-cell (PUC) grid-connected inverter is presented. The control method is derived based on sliding mode control theory. Making use of the measured grid voltage and computed reference grid current, the reference value of the inverter output voltage is determined. According to the calculated voltage value, the suitable control set is selected (offline) in order to satisfy the reaching conditions of two separate sliding lines. The sliding lines are defined based on the grid current and the auxiliary capacitor voltage errors. Simulation results are presented to show the effectiveness of the proposed simple controller in tracking the desired values.

Temperature forecasting based on meteorological data is the key stage for an accurate estimation of PV power production and demand-side management leading to better grid stability. Typically, weather forecasting is the prediction of weather parameters for seconds until months ahead based on the historical weather database. Thus, researchers create several approaches to maximize the accuracy of these predictions and increase the period of estimation. This paper proposes a new medium and long-term temperature forecasting approach based on Multi Inputs Single Output (MISO) model base on empirical equations. The parameters of the proposed model are computed using a Recursive Least Squares (RLS) method. Using a set of real meteorological data, simulation results are presented to show the high accuracy of the proposed temperature forecasting approach.

Compared to other multilevel inverter (MLI) topologies, the Packed U -Cells (PUC) inverter is capable of generating more levels with less components. Thus, this paper investigates the operation of a 5-level PUC (PUC5) inverter supplying an RL load in both open-loop and closed-loop operations based on its averaged dynamical model. The aim is to prove that the self-balancing, a natural feature of the converter, could be obtained by the selection of a regular phase-shift between two carrier signals when applying a Pulse Width Modulation (PWM) technique. Moreover, a nonlinear feedback scheme is proposed to ensure fast balancing of the capacitor voltage and good tracking of the output voltage reference. Simulations results are presented to show the performance of the proposed techniques in ensuring self-balancing of the capacitor voltage and high tracking quality of the desired values.

The deployment of a high-reliability Multilevel Inverter (MLI) able to operate effectively and unremittingly in case of partial failure is indispensable in high performance and high power grid-connected PV systems. Among the well-known MLI topologies, the Cascaded H-Bridge (CHB) inverter is characterized by its high modularity and fault-tolerance features due to the high flexibility in generating the output voltage levels. However, the effective operation of such topology needs a tight supervision to detect the failure and provide uninterrupted power supply. This paper proposes a novel open-circuit failure detection strategy based on a 1st-Order Dynamic Gains Robust Differentiator (DGRD) for 3-phase 9-cell CHB inverter. The proposed scheme estimates the current 1st derivative in order to easily detect the amplified faults. This technique is considered as an improved version of the sliding mode based Levant derivative approach, where a constant gain is used instead. The presented simulation results confirm the accuracy of the proposed technique in real-time fault detection (distinction between noises and faults to avoid false alarms and misdetection).

The power decoupling strategies of single-phase Z-source/quasi-Z-source inverter (ZSI/qZSI) to handle the inherent double-line-frequency (2ω) power are overviewed. They are categorized as passive and active power decoupling methods. The former suppresses the 2ω ripple by the impedance network, modified modulation methods, or closed-loop damping control. The latter diverts the 2ω ripple to the compensation capacitor of integrated active power filters, through properly operating a half-bridge phase leg. The pros and cons of those 2ω power decoupling methods are discussed and recommendations are given for future development of such single-phase inverters.

This paper proposes a Current Deadbeat Control (DBC) algorithm for a three-phase 9-cells qZS based Cascaded H-Bridge (CHB) inverter to ensure grid current supply with low THD. The studied topology is considered as a single-stage DC/AC topology having the capability of boosting the DC input voltage with high-quality multilevel AC voltage, independent DC-link voltage compensation, and control of the power transfer with high reliability. Moreover, a Repetitive Controller (RC) is connected to the inductor current loop with the aim to reject periodic disturbances (double line-frequency fluctuations). The employed RC has the capability of learning through iterations based on tracking error. During the control, the compensation method uses the error between the DC-link peak voltage reference and the actual value to produce the compensating inductor current reference. Theoretical analysis and simulation results shows that the proposed solution achieves high dynamic performance and reduced cyclical disturbances effects.

This paper presents a grid-connected five-level neutral-point-clamped (NPC)/H-Bridge quasi-impedance source inverter (qZSI) and its control scheme for use in PV applications. The proposed topology is combination of a three-phase qZS network and NPC/H-Bridge inverter that is suitable for high power applications. The proposed inverter transfers dc power into the ac power without dc/dc boost converter. To control the proposed topology, PI based control scheme is developed. A modified level shifted sinusoidal pulse width modulation (LS-SPWM) technique is employed to generate gate signals for inverter. The effective and simple control algorithm is also employed to regulate the inverter output current. The performance of the proposed system has been validated by simulation results. These results show excellent performance in terms of steady-state and dynamic response.

Grid-connected energy storage systems have been implemented in ac power systems as uninterruptable power supplies (UPS). Batteries and bi-directional power converters provide electrical power when off-grid and recharge when grid-connected. In this paper, a packed U cells (PUC) seven-level inverter has been selected as the grid-interface due to the lower cost and fewer number of components compared to other bi-directional topologies. Additionally, the PUC has higher power quality when compared to the traditional H-bridge. Compared to the traditional PI controller, Model Predictive Control (MPC) is attracting more interest due to its good dynamic response and high accuracy of reference tracking. Through the minimization of a user-defined cost function, the proposed MPC technique can simultaneously achieve unity power factor, low total harmonics distortion of the grid-side current and balance the PUC capacitor's voltages at the grid side, and control bi-directional power flow in the batteries-PUC system. The presented topology and proposed control technique are verified by simulating a 600 W reduced-scale prototype. The theoretical principles are validated by implementing the controller on the prototype using dSPACE 1007 platform.

This paper proposes a weighted Real-Time Digital Feedback Control for a grid connected Distributed Generation (DG) system (gas turbines, fuel cells, photovoltaic, wind…). The DG source is connected to the grid through a Power Conditioning Unit (PCU) consisting of a multilevel Flying Capacitors Inverter (FCI), an inductor, and a step-up transformer. The proposed controller is predictive type offering the possibility of furthering the capacitor voltages tracking, load current tracking, or a trade-off between all state variables tracking (capacitor voltages should be controlled in order to ensure a proper operation of the FCI). The Weighted Predictive Controller (WPC) transfers the power to the grid, even under grid voltage variation (sag or swell) due to its Voltage Ride Through (VRT) capability by controlling the current with unity power factor. Theoretical analysis, simulation and experimental results are described in this paper.

PV energy generation has been one of the most active research areas in the past decades due to its inexhaustibility and environmental-friendly aspects. However, the great deal is to utilize the solar energy effectively through power electronics converters (conditioners) to meet the increasing demand for load. This paper proposes an experimental photovoltaic (PV) power conditioning system with line connection. The conditioner consists of a Flying Capacitors Inverter (FCI) feeding an inductive load and connected to the grid through transformer. A hybrid model of the power conditioning system is derived using the electrical equations and verified by a commuted Bond Graph (BG) method. The obtained hybrid model is an explicit standard model valid for all FCI configurations. An original controller based on weighted real-time predictive control technique is designed and its implementation is made simple using the derived hybrid model. The control algorithm transfers the power to the grid by controlling the current with unity power factor. The proposed controller transfers the power to the grid with lower harmonics content. The theoretical analysis, the simulation results and the experimental results are described in this paper.