This paper proposes a hybrid model predictive control (HMPC) strategy for a three-level neutral-point-clamped (3L NPC) rectifier to optimize the operation of electrolyzers for green hydrogen production. Unlike conventional approaches that focus primarily on inverter applications, the proposed method directly addresses the critical issue of neutral-point voltage imbalance in NPC rectifiers. The control framework combines discrete switching-state selection with adaptive objective prioritization, utilizing real-time capacitor voltage measurements to exploit switching-state redundancy. A multi-objective cost function balances current tracking, DC-link voltage regulation, neutral-point voltage balancing, and switching-loss minimization. An adaptive weighting mechanism dynamically prioritizes control objectives in response to system conditions such as renewable intermittency and grid disturbances. To improve long-term reliability, the strategy introduces state diversity enforcement and inactivity penalties, reducing capacitor stress without compromising harmonic performance. The effectiveness of the proposed method is evaluated using a dynamic equivalent model of a proton exchange membrane (PEM) electrolyzer, enabling a realistic assessment of green hydrogen production. Validation through hardware in the loop (HIL) test demonstrates robust current control and stable DC-link voltages in varying operating scenarios.

This paper proposes a hybrid digital twin framework that couples a real-time physics-based digital twin model with a data-driven diagnostic layer implemented through cloud-based data acquisition and analysis. This framework generates synthetic datasets across multiple speed levels and fault severities for bearing fault detection and classification in industrial spindle systems, where real fault recordings are costly, risky, and difficult to reproduce. Once the system is validated, a two-stage classifier is trained and used for online fault detection and fault-type identification, whereas the deep-sequence model provides offline verification. To improve robustness, training data are enhanced with multi-domain feature enrichment and targeted data augmentation techniques that simulate measurement noise and small operating variations. The resulting models achieved strong performance under previously unseen operating conditions within the validated digital twin envelope. Overall, the proposed approach reduces the dependence on real fault experiments by enabling the risk-reduced development and evaluation of data-driven bearing fault diagnosis.

This article introduces a parallel differential power processing (PDPP) architecture for photovoltaic (PV)/battery applications. The PV to Virtual Bus (PV2VB) architecture enables the integration of a battery and manages its power while performing maximum power point tracking on the PV strings. In the proposed PV2VB PDPP architecture, the battery is positioned at the virtual bus, acting as the input for all string-level converters (SLCs). By selecting a lower voltage for the battery at the virtual bus compared to the PV string or the main bus voltages, component voltage ratings can be reduced. The architecture employs dual active bridge converters connected to bridgeless (BL) converters as SLCs to generate both positive and negative output voltages while providing isolation. These SLCs track the maximum power point of each PV string, while the central converter manages battery charging and discharging. Experimental results confirm the performance and effectiveness of the proposed PV2VB PDPP architecture, achieving efficiencies between 95.5% and 99%.

Power electronic converters are essential components in modern electrical systems, with dc–dc converters being particularly crucial for their extensive variety and widespread applications, notably in renewable energy systems and electric vehicles. For applications such as Photovoltaic (PV) arrays, fuel cells, and microgrids, a critical requirement is the need for non-isolated, high step-up dc–dc converters capable of providing a high-voltage gain while mitigating significant practical challenges, including high semiconductor stress and degraded efficiency at extreme duty cycles. This study proposes a comprehensive, structural-based review of over 100 non-isolated high step-up dc–dc converters, specifically excluding topologies that rely on coupled inductors. Our primary contribution is a novel categorization and comparative analysis framework that delves into the fundamental topological details and features, going beyond the focus of recently suggested reviews. The methodology begins with a systematic analysis of classical non-isolated converters (Boost, Buck-Boost, Ćuk, SEPIC, and Zeta) to establish a baseline and highlight their limitations, such as the semiconductor voltage stress being greater than the output voltage in most cases, which reduces their suitability for high-gain applications. The paper then systematically classifies advanced topologies into 23 distinct groups based on their unique structural characteristics. The comparison is rigorously quantitative and systematic, focusing on structural details and key performance metrics such as voltage gain and density, semiconductor stress, and current continuity and component count. The comprehensive analysis is conducted by deconstructing each topology into its constituent sub-converters to reveal how structural combinations influence key performance metrics. Finally, the findings facilitate a discussion on the practical applications of each topology, matching their inherent characteristics to specific real-...

This study presents a data-driven offline digital twin model of an operational residential hydrogen hub equipped with more than 100 sensors. The model enables analysis and scaling of hydrogen-based hybrid energy hubs from residential to larger systems. The hub integrates photovoltaic generation, battery storage, hydrogen production via electrolysis, compressed hydrogen storage, and fuel cell electricity generation. Using year-long field data, the model reproduces the current configuration (5.34 kWp PV, 15 kWh battery, ~ 45 kg H₂) and quantifies annual performance: 5,102 kWh PV generation, hydrogen production and consumption efficiencies of 48.0% and 39.6%, 25.3 kg H₂ produced versus 49.3 kg consumed, and net grid exchange of +114 kWh. Multi-scenario sizing shows that an optimized configuration (8.46 kWp PV, 30 kWh battery, 70 kg H₂) reduces grid import to ~ 30 kWh yr^-1 while exporting ~ 380 kWh, with the H₂ buffer ending the year near its initial state under a rule-based energy management strategy. The results demonstrate the capability of a sensor-validated framework for designing integrated PV-battery-hydrogen energy hubs.

ABSTRACT The reliable performance of multilevel inverters (MLIs) is critical to the advancement of power electronic systems used in renewable energy conversion, microgrid operation, and electric vehicle technologies. However, these systems are often susceptible to open-circuit faults in power switches, which can adversely affect output waveform quality and overall system stability if not detected early. This study presents an intelligent fault detection and localization strategy based on a fuzzy inference system (FIS) for a reduced device count cross-connected-source MLI. The proposed diagnostic framework employs output voltage and current signals as diagnostic parameters, enabling precise identification of fault conditions. A comprehensive set of fuzzy rules is developed to differentiate between single and double switch faults under diverse fault inception angles. Real-time validation is carried out using the dSPACE DS1202 controller in a hardware-in-the-loop environment. The findings confirm that the proposed FIS-based approach achieves high fault classification accuracy, rapid response time, and strong adaptability across varying operating conditions, highlighting its suitability for real-time condition monitoring in modern power conversion systems.

Multilevel inverters (MLIs) have become key enablers in renewable energy (RE) integration and electric vehicle (EV) systems, where high-quality power conversion and robustness are critical. Among the different topologies, the Crossover Switches Cell (CSC) converter has recently gained attention due to its superior voltage-boosting capability and reduced component count. While most existing studies on CSC control strategies have been limited to simulations, this work advances the field by providing comprehensive real-time experimental validation under varying operating conditions and parameter mismatches. Finite Control Set Model Predictive Control (FCS-MPC), Sliding Mode Control (SMC), and Lyapunov-based MPC (LMPC) are comparatively assessed in terms of dynamic response, voltage regulation, harmonic minimization, and robustness. Real-time implementation on an Opal-RT platform demonstrates that MPC achieves superior current control with minimal harmonics, SMC offers strong disturbance rejection and effective capacitor voltage balancing, while LMPC guaranties stability with a reduced computational burden. The presented results highlight the trade-offs between these advanced control strategies while providing practical guidelines for selecting robust control techniques for grid-connected MLIs in RE and EV applications.

This article provides a comprehensive review of power electronics converter control and energy management for hydrogen production systems through water electrolysis. Hydrogen production from renewable energy sources is a key pathway toward decarbonizing energy systems and enabling large-scale energy storage. Efficient and dependable operation necessitates addressing the dynamic properties of electrolyzers, the intermittent nature of renewable sources, and the coordination among numerous power electronic interfaces. Unlike earlier studies that addressed these aspects separately, this review systematically connects electrolyzer modeling, converter design, control architectures, and energy management to reveal their critical interdependence. By examining these connections, the analysis reveals critical research gaps in real-time coordination, parameter adaptation, and scalable architectures, outlining pathways toward intelligent and grid-independent hydrogen production systems. This review integrates electrolyzer modeling, power converter control algorithms, AC and DC energy hub architectures, hierarchical control schemes, and energy management systems from classical to advanced methods.

This paper proposes an innovative model-free deep reinforcement learning-based controller (RL-C) for a grid-connected 5-level packed-U-cell (PUC5) multilevel inverter (MLI). The controller is designed to deliver a high-quality grid current while maintaining the PUC5 floating capacitor voltage at its reference level. In addition, the proposed controller supports both active and reactive power exchanges, adapts to variations in voltage and current references, and remains robust under grid voltage variations. The RL agent learns optimal switching actions through direct interaction with the PUC5 system, eliminating the need for data collection or reliance on existing control models. An Actor-Critic architecture is adopted, and the Proximal Policy Optimization (PPO) algorithm is applied for training (offline) using MATLAB/Simulink, where the RL-C is evaluated under diverse PUC5 configurations and operating conditions in the testing phase. The trained agent has been implemented on an Opal-RT real-time system and validated experimentally using a laboratory-made PUC5 prototype. The performance of the proposed RL-C approach is compared to both traditional approaches including finite control set model predictive control, sliding mode control, and PI control, and other state-of-the-art RL algorithms, demonstrating superior generalization and training efficiency. Moreover, a sensitivity analysis quantifying the impact of reward design, state space, network size, and key hyperparameters on convergence and performance is carried out.

This paper assesses a Hybrid Energy Storage System (HESS) at The Green Village (TGV) of Delft University of Technology (TU Delft), designed and developed as a combination of a lithium-ion battery and hydrogen storage systems to provide a residential energy supply. This paper will evaluate the combination of producing solar-powered green hydrogen through electrolysis, as well as daily and seasonal combinations of battery and hydrogen storage, and electricity generation through a fuel cell. Through an analysis of various sensor data that contained power and hydrogen flow, and control signals, this case study reports on the overall efficiency of the HESS, encourages user energy balancing strategies, and assesses its capability to store sustainable energy over long periods. Based on the same dataset, preliminary machine learning models have been developed and evaluated to predict hydrogen production from weather and PV inverter measurements, supporting future EMS optimization. Therefore, this case study indicates improvements that led to key observations.

Renewable Energy Sources (RESs), particularly Photovoltaic (PV) systems, inherently produce variable DC voltages that often cannot meet load or grid requirements directly. Consequently, a reliable and efficient DC-DC converter is required to interface RESs with downstream converters and loads. This paper proposes a non-isolated, high-gain, transformerless step-up DC-DC converter that provides continuous input current and a non-inverting output voltage. The proposed topology is designed to minimize switching losses and to maintain a low component count, thereby improving conversion efficiency while containing cost and implementation complexity. A comprehensive analytical model that includes parasitic elements is developed to derive the converter voltage gain. Comparative analyses of device voltage and current stresses against recently reported topologies are presented. The results demonstrate that the proposed converter achieves high voltage gain with reduced voltage and current stresses on switching devices, while preserving acceptable component current levels. Conduction intervals of switches and diodes, as well as switching loss contributions, are analyzed and quantified. Experimental validation is provided by a 100 W prototype, and measured results corroborate the theoretical predictions and simulation outcomes. The proposed converter thus represents an effective and practical solution for high-gain DC–DC conversion in renewable energy applications, offering an advantageous trade-off between efficiency, component simplicity, and cost.

This paper introduces a soft-switched high step-up converter applicable to photovoltaic systems. In the proposed topology, to further improve the voltage gain and reduce the components voltage stress, coupled inductors and voltage multiplier cell (VMC) techniques are integrated with the conventional boost converter. Hence, it addresses challenges in traditional boost converter when operating at near unity duty cycle and enables it to utilize high-quality components that lead to decreased conduction losses in high-output voltage applications. Furthermore, the converter achieves soft switching operation by incorporating a lossless snubber cell, which consists of just two diodes and requires no additional switches and magnetic cores. These features contribute to enhancing the converter efficiency. Notably, the energy stored in the snubber circuit is effectively recovered to the output without any circulating current, making it a beneficial characteristic among the lossless snubber structures. The proposed topology also offers a common ground between the input and output terminals, as well as the switch which simplifies the converter control circuit. Additionally, it maintains continuous input current, which makes the proposed converter more suitable for photovoltaic system applications. To validate the converter benefits, a 150 W laboratory prototype converter is implemented.

This article presents a fully soft switched, non-isolated high voltage gain single magnetic core multiport converter based on boost three port structure for renewable energy applications. The developed voltage multiplier cell integrates switched capacitor and coupled inductors techniques to achieve high voltage gain and more design freedom. Furthermore, only a single magnetic core is employed which contributes to higher power density while an active clamp cell is integrated to provide fully soft switching condition and eliminate capacitive turn-on loss. All switches operate at zero voltage switching and diodes turn off at zero current switching. This approach minimizes switching losses and clamps the voltage spikes which enables the use of low forward voltage diodes. Also, the switches voltage stress is reduced through utilizing coupled inductors technique and thus, switches with low drain-source resistance can be utilized to achieve high efficiency along with high power density. To validate the theoretical analysis, a 200 W prototype with 400 V output port is implemented and the experimental results are presented.

This paper introduces a novel single-loop control scheme for voltage regulation in islanded inverters, using a proportional-integral-lead (PI-Lead) controller designed within the synchronous reference frame (SRF) through a loop-shaping approach. Commonly, dual-loop controllers have been employed for this purpose owing to several limitations, such as insufficient stability with a narrow gain margin, a trade-off between stability and bandwidth, and constrained bandwidth due to the need for a significantly lower outer voltage loop bandwidth compared to the inner current one. The proposed method overcomes these challenges by integrating a Lead compensator, which enhances voltage regulation by eliminating steady-state error, improving stability margins, and providing a fast transient response while maintaining robustness against model parameter variations. Additionally, the control strategy reduces dependence on current measurements, except when dealing with inductive loads where virtual resistor-based active damping is necessary. Despite the challenges posed by multi-resonance phenomena and coupling effects inherent in single-loop SRF-based modeling, a comprehensive frequency-domain analysis is performed, with systematic controller parameter design guidelines to mitigate multi-gain crossover issues. Rigorous experimental results validate the theoretical findings and simulations, demonstrating the superior performance and practical effectiveness of the proposed control strategy compared to existing methods.

In the contemporary landscape of trend industries including sustainable energy sources, high-voltage direct current, and electrified mobility, the need for power conversion units to bridge disparate sections is felt more than ever before. Among these conversion units, the step-up DC-DC converters occupy a pivotal role, elevating the DC voltage levels and facilitating interactions between converters and circuits. However, the multistage DC-DC converters, prevalent in large-scale industries, offer higher voltage gains and power density. This review paper has categorized the multistage DC-DC converters into isolated/non-isolated, voltage-fed/current-fed, unidirectional/bidirectional, hard-switched/soft-switched, and step-up/step-down configurations. It has been followed by a brief review of various voltage boosting techniques, containing an analysis of multi-staging voltage boosting methods and recent advances in converter structures. Then, the multistage DC-DC converters have been classified into several distinct categories: quadratic gain, cascaded, interleaved, modular, multilevel, and hybrid structures. Recent advancements and developments in each of these categories have been meticulously examined, with a focus on their fundamental concepts, advantages, and disadvantages. In particular, the voltage gains, voltage stresses, and current stresses associated with quadratic gain and cascaded DC-DC converters have been analyzed and compared in detail. Furthermore, an in-depth exploration of the structures and configurations of interleaved, modular, and multilevel DC-DC converters has been conducted. This includes a discussion on the combinations of modules, the benefits arising from these integrations, and insights for future developments. The applications of each category of multistage DC-DC converters across various industries—particularly in grid applications—have been thoroughly analyzed. Subsequently, these converters have been evaluated based on several criteria: reliability, component count, control complexity, voltage gain, power level, cost, and weight. The prioritization of these factors has also been systematically presented.

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.

Luo converters represent the most extensive family of non-isolated DC-DC converters, yet achieving high voltage gain in these topologies often compromises simplicity. This study introduces a novel family of quadratic boost converters that address this challenge by combining six key features: 1) continuous input current, 2) reduced component count, 3) optimized voltage/current stress on semiconductors, 4) higher voltage gain than conventional designs, 5) high efficiency across a wide duty-cycle range, and 6) topological extensibility comparable to Luo converters. The proposed topologies are analyzed in both ideal and non-ideal operating modes, with derived expressions for voltage gain and efficiency sensitivity. Design requirements are detailed for continuous conduction mode (CCM), complemented by small-signal analysis and compensator design for the primary family members. Experimental results validate the theoretical models, demonstrating the converters’ performance and robustness. Additionally, voltage gain tests for extended topologies confirm their adherence to theoretical predictions. The proposed family offers a streamlined, high-performance alternative to existing Luo converters, with potential applications in high-gain, non-isolated power conversion systems.

Electric vehicles (EVs) offer a promising solution for mitigating greenhouse gas emissions and minimizing the transportation sector's dependency on non-renewable energy sources. However, efficient energy management poses a significant challenge for their broader adoption, particularly optimizing battery usage, maximizing driving range, and improving overall vehicle performance. This paper presents the state-of-the-art Artificial Intelligence (AI) techniques used in electric vehicle energy management systems (EV-EMS), discussing a variety of deep learning algorithms of AI methodologies, such as, neural networks, and fuzzy logic. Additionally, This paper discusses the role of auxiliary techniques like transfer learning, which enhances model adaptability and reduces training time in AI-driven EMS applications. Through a systematic analysis of each method, this review identifies key trends, highlights the challenges and limitations of each technique, and offers perspectives on potential solutions and future research directions. The paper aims to support researchers, industry professionals, and policymakers in developing advanced, sustainable, and adaptable EV-EMS solutions that maximize battery life, improve vehicle performance, and facilitate real-time adaptive control. Finally, this review highlights the importance of AI-driven strategies in making EV technology more efficient, reliable, and scalable.

The power versus voltage curve of a photovoltaic (PV) panel exhibits several maximum power points (MPPs) in a partial shading (PS) condition. Thus, it remains an optimization challenge to ensure that PV systems operate at their global MPP (GMPP). Scanning the output characteristics of the PV panels seems a general solution for this issue. However, applying a short circuit to the terminal of PV panels where there exists an electrolytic capacitor, has a detrimental effect on the lifetime of the system. To this end, in this article, a GMPP estimator is proposed as a global solution for conventional maximum power point tracking (MPPT) algorithms under PS conditions. The proposed technique improves existing simple MPPT algorithms with original approaches as follows: first, an accurate microscopic analysis of a PV characteristic in PS conditions is considered, second, an original definition of the dominant cells and modules in a PV panel is proposed that allows to reduce the PS patterns to a finite number, and third, the search area for the MPPT operation is reduced to find the accurate GMPP by proposing two voltage boundaries. The lower boundary corresponds to the GMPP under uniform shading condition that can be determined using a closed form formula, while the upper one refers to the GMPP of a dominant cell in a PV module that can be determined using an artificial intelligence technique. This can also help set the initial duty cycle in a convex area around the GMPP. The functionality of the proposed GMPP estimator is experimentally validated.

Electrolyzers operate as nonlinear low-voltage high-current loads, presenting significant challenges for power conversion systems. This article investigates state-of-the-art modulation schemes for the dual-active-bridge (DAB) converters, focusing on their performance and interaction with electrolyzer loads in electrolysis applications. The behavior of electrolyzers is compared with that of conventional constant voltage loads across various modulation schemes, using peak primary transformer current as the evaluation metric at three power levels: 1, 10, and 100 kW. A peak current optimization strategy tailored for electrolysis applications is proposed. Based on this, an optimized operating trajectory for the DAB converter during electrolysis is identified for each power level. The optimization results are validated experimentally using a 1-kW 20-kHz prototype, and through MATLAB simulations for the 10- and 100-kW systems. The proposed approach achieves peak current reductions of up to 15.75% at 100 W for the 1-kW system, 42.71% at 1 kW for the 10-kW system, and 60% at 10 kW for the 100-kW system, demonstrating its effectiveness in improving DAB converter performance for electrolysis applications.