Ning Wang

In order to ensure continuous DC link current, the overlap time introduced in the switching process of the current source rectifier (CSR) can lead to undesirable harmonics and reduce grid-side current quality. To address this issue, a hybrid space vector modulation (HSVM) method is proposed. This method designs two current vector sequence modes by reconstructing the sequence of the dwell current vectors. Then, an adaptive threshold decision (ATD) method is introduced, which can dynamically select two sequence modes during the modulation process to solve the current sharing phenomenon. The proposed method no longer need to add overlap time in modulation and fundamentally eliminates the overlap time effect (OTE). Moreover, it can minimize the switching times by using reverse voltage to block the on-state switches. By comparing with the ideal five-segment space vector modulation, theoretical analysis, simulation and experimental results prove the effectiveness of HSVM in improving system performance and minimizing the number of switching operations.

While green hydrogen is vital for sustainable energy transitions, the volatility of renewable power adversely affects the dynamic operation and service life of electrolyzers in integrated energy systems (IESs). To mitigate these effects while minimizing operational costs and extending the service life of electrolyzers, this paper proposes an optimization method for the operation of IESs that considers the dynamic operating characteristics and lifetime degradation of multiple types of electrolyzers. Firstly, detailed models for alkaline (ALK) electrolyzer and proton exchange membrane (PEM) electrolyzer are developed, and their start–stop characteristics and lifetime degradation characteristics are analyzed. Secondly, an optimal operation model for IES is established, taking economy as the optimization objective and considering the dynamic operating characteristics and lifetime degradation of multiple types of electrolyzers. By piecewise linearizing the hydrogen production rate of the electrolyzer, the original model is transformed into a mixed-integer linear programming model for solution. The results indicate that the proposed method can reduce the operational costs of IES, increase the proportion of stable operation time for the electrolyzer, decrease the number of startups and shutdowns, subsequently reduce the cost associated with the lifetime degradation of the electrolyzer, and specifically extend the actual lifetime of the PEM electrolyzer by 12.17% versus its rated life. Ultimately, this approach not only improves the economic viability of the system but also ensures the long-term sustainability of green hydrogen projects by minimizing equipment replacement cycles and maximizing renewable energy accommodation.

Off-grid wind-to-hydrogen systems are considered a promising solution for sustainable, large-scale green hydrogen production in remote areas. However, under the combined effects of highly fluctuating wind generation and stochastic load variations, existing energy management methods still face a challenge: in off-grid wind-to-hydrogen systems, intelligent energy management studies that jointly address economic performance and operational stability are still limited. To address these issues, this paper develops a mathematical model for an off-grid wind-to-hydrogen system to reveal the coupling characteristics of the wind–electricity–hydrogen conversion process. Building on this model, a deep-reinforcement-learning-based energy management strategy is proposed. By formulating objectives that simultaneously capture economic benefits and stability requirements, the proposed strategy enables adaptive power flow allocation and dynamic optimization under uncertainty. Case studies demonstrate that, while fully satisfying load demand, the proposed strategy can significantly improve renewable energy utilization and hydrogen production, thereby increasing profit and ensuring stable and sustainable system operation.

Among various production methods, hydrogen generation via electrolysis powered by renewable energy plays a key role in achieving large-scale green hydrogen production. The triple active bridge isolated dc–dc conversion system exhibits significant application potential in hydrogen production due to its advantages, such as high energy density, wide step-down ratio, and high reliability. However, the output current ripple at the hydrogen production port critically affects the efficiency of the electrolyzer and the hydrogen production rate. Existing studies have limited optimization effects on current ripple and struggle to achieve dynamic optimization, leading to constrained ripple suppression under dynamic operating conditions. To address this issue, this article proposes a low-ripple modulation strategy based on coordinated optimization of inner and outer phase-shift angles for multiport power conversion systems in renewable energy hydrogen production. By establishing an accurate mathematical model, the optimal phase-shift angle combination under minimal current ripple conditions is derived. An improved differential evolution algorithm with adaptive parameter strategy is employed to achieve global optimization under dynamic conditions. Simulation and experimental results demonstrate that the proposed strategy effectively suppresses current ripple, providing an efficient and reliable solution for hydrogen production from fluctuating renewable energy sources.

Three-phase current source inverters (CSIs) generate common-mode voltage (CMV) due to rapid switching, which can cause insulation degradation, motor bearing wear, and electromagnetic interference. The active zero-state (AZS) modulation technique mitigates CMV by substituting zero vectors with opposing active vectors but introduces increased switching harmonics, particularly at low modulation indices. This article analyzes the voltage harmonics of conventional five-segment modulation (C-SVM) and AZS modulation using a harmonic charge trajectory-based approach. Two improved AZS modulation strategies are proposed, optimizing the dwell time of active vectors to minimize the root mean square (rms) value of the harmonic charge, thereby reducing switching harmonics. Experimental results validate the effectiveness of the proposed methods.

To reduce the number of sensors in the three-phase current source inverter and lower its cost and size, using a single dc-Link voltage sensor to reconstruct the three-phase ac voltage is a simple and effective solution. However, due to nonideal factors during the sampling process, accurate dc-Link voltage measurement cannot always be achieved, especially at the sector boundaries and low modulation index, where the dwell time of the active vector is short, which leads to voltage reconstruction failure. To address this issue, this article proposes an improved voltage reconstruction method based on virtual space vector modulation. The modulation index is divided into low, medium, and high regions according to constraint conditions. In the low modulation region, the reference current vector is synthesized from virtual vectors constructed from three adjacent active vectors. In the medium region, the virtual vectors are formed by two adjacent active vectors. In the high modulation region, the reference current vector is directly synthesized using three adjacent active vectors. This ensures that the dwell time of the sampled current vectors exceeds the minimum required sampling time. The proposed method reduces the measurement dead zone and can achieve three-phase voltage reconstruction under wide/full modulation index range. In addition, this method can also reduce the amplitude and lower-order harmonics of common-mode voltage. Experimental results validate the effectiveness of the proposed voltage reconstruction method.

The single-stage three-phase ac/dc matrix dual active bridge (M-DAB) converter, characterized by its high voltage gain and extensive control freedom, exhibits complex circuit characteristics, which have resulted in limited research into soft switching control under light load conditions on its H-bridge side. Furthermore, soft switching control on the H-bridge side leads to increased inductor current amplitude, thereby elevating current stress, thus increasing the volume of filters and reducing the lifespan of loads. To tackle these challenges, a hybrid modulation strategy for the M-DAB converter is developed in this research. This strategy incorporates the analysis of parasitic parameters of switching devices to define the conditions for achieving soft switching and proposes a method to optimize the amplitude of inductor currents based on current characteristics, which not only achieves soft switching on the H-bridge side but also reduces the amplitude of the inductor currents. Finally, by analyzing simulation and experimental data and comparing them with existing studies, verified that the proposed hybrid modulation strategy can optimize the current by more than 70%, demonstrating the effectiveness of the proposed strategy.