SinoGreenTech Academic Portal
Open AccessDOI: 10.19912/j.0254-0096.tynxb.202608_9670Original Research

Three-Phase Parallel Quasi-Z-Source Low-Ripple High-Gain Grid-Connected Converter for Hydrogen Fuel Cells

State Key Laboratory of Disaster Prevention & Reduction for Power Grid (Changsha University of Science & Technology)

Read Executive PreviewQuick FAQ
Three-Phase Parallel Quasi-Z-Source Low-Ripple High-Gain Grid-Connected Converter for Hydrogen Fuel Cells
Graphical Abstract / Figure
Published In
Acta Energiae Solaris Sinica
Published:January 15, 2026Edition:Vol. 47, Issue 8 • pp. 100-112Citation:JIANG Fei et al. (2026), Acta Energiae Solaris Sinica
Impact FactorPeer-Reviewed Core
Source Journal太阳能学报
Strategic Intelligence Pillar
Water Electrolysis for Green Hydrogen: Low-Iridium PEM & High-Pressure Alkaline Systems
Explore Topic Pillar

Key Takeaways & Executive Findings

  • • • The PEMFC stack exhibits a voltage swing from 213 V to 323 V and output current up to 697 A at 87.965 kW rated power, necessitating a converter with wide input range and high current handling capability; the proposed three-phase parallel quasi-Z-source topology reduces per-phase current stress by a factor of three, directly mitigating switch overcurrent risk and improving system reliability. • • Traditional interleaved parallel structures degrade ripple suppression at high voltage gain, but the proposed quasi-Z-source network with three-phase interleaving achieves low input current ripple without relying solely on large electrolytic capacitors, which are prone to aging (capacitor aging cited as a key failure mode in fuel cell systems, with lifetime impacted by ripple), thereby extending fuel cell operational life. • • The two-stage architecture (DC-boost + inverter) with a parallel battery energy storage (Vbat) on the DC bus addresses the slow dynamic response (second-level) of PEMFCs, preventing overload and load shedding during grid disturbances; this hybrid configuration ensures DC bus voltage stability under complex grid conditions. • • The proposed converter achieves high voltage gain at lower duty cycles and reduced turns ratios compared to conventional boost converters, as evidenced by the quasi-Z-source network's inherent boost capability; this reduces the voltage stress on power devices and allows the use of lower-rated, cost-effective IGBTs, improving overall system economics for high-power hydrogen fuel cell grid integration.
Weekly Academic Intelligence

China Clean Energy & Battery Radar

Get verified English translations, SEM micrographs & open-access PDF alerts from China's leading state key laboratories delivered to your inbox every Monday at 08:00 EST.

Institutional privacy protected100% Free Open AccessUnsubscribe anytime

Abstract

Existing grid-connected converter systems for hydrogen fuel cells suffer from insufficient voltage step-up capability, weak ripple suppression, and elevated switch overcurrent risk. This paper proposes a three-phase parallel quasi-Z-source two-stage DC-boost hydrogen fuel cell grid-connected converter (TPPQZSTSDBHFC-GCC). A mathematical model of proton exchange membrane fuel cell (PEMFC) output voltage, current, and power is established to reveal the power-voltage-current relationship and response characteristics. Based on the fuel cell output characteristics, the TPPQZSTSDBHFC-GCC topology is proposed, and its operating principle and ripple suppression capability are analyzed in detail. Results demonstrate that the proposed topology achieves high voltage gain while maintaining low ripple, low overcurrent risk, and superior steady-state performance. Simulation and experimental verification confirm the correctness and effectiveness of the proposed topology. The system addresses the critical challenge of wide-range voltage fluctuations (213–323 V) and high output currents (up to 700 A) in high-power PEMFC stacks, enabling safe grid integration and extended fuel cell lifespan.

1. Introduction

Hydrogen fuel cell grid-connected power generation is a critical pathway for hydrogen energy utilization, yet high-power proton exchange membrane fuel cells (PEMFCs) exhibit highly nonlinear volt-ampere characteristics, low output voltage, high current, and significant fluctuations. These traits render direct connection to loads infeasible; dedicated DC-DC converters and inverters are required for power conditioning. Existing research has largely overlooked the operational characteristics of fuel cells, resulting in suboptimal power generation and compromised system reliability. Specifically, conventional single-stage boost converters suffer from insufficient voltage gain, weak ripple suppression, and elevated switch overcurrent risk, which are exacerbated by the wide voltage range (213–323 V) and high currents (up to 700 A) of practical PEMFC stacks.

To address these bottlenecks, this paper proposes a three-phase parallel quasi-Z-source two-stage DC-boost hydrogen fuel cell grid-connected converter (TPPQZSTSDBHFC-GCC). The topology integrates a three-phase interleaved quasi-Z-source network with a two-level inverter and LC filter, supplemented by a battery energy storage system on the DC bus to buffer the slow dynamic response of the fuel cell. By establishing a mathematical model of the PEMFC output characteristics and analyzing the operating principle of the proposed converter, this work demonstrates that the topology achieves high voltage gain, low input current ripple, and reduced switch current stress. Simulation and experimental results validate the effectiveness of the proposed solution, offering a robust technical pathway for safe and efficient hydrogen fuel cell grid integration.

SinoTechIntel Interactive Document Reader
Page 1–5 of Preview
100%
Download Full PDF

Loading authentic research manuscript (Pages 1–5)...

Cite This Research Paper
JIANG Fei, TANG Hao, MAIMAITIAILI Wufuer, HUA Dong, HE Guixiong, GAO Jiayuan (2026). Three-Phase Parallel Quasi-Z-Source Low-Ripple High-Gain Grid-Connected Converter for Hydrogen Fuel Cells. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9670
SinoGreenTech Academic & Legal Disclaimer

Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.

Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.

Frequently Asked Questions

What specific failure mechanisms in PEMFCs are mitigated by the proposed converter's ripple suppression?

High current ripple accelerates degradation of the PEMFC membrane and catalyst layer, leading to reduced lifespan. The proposed three-phase interleaved quasi-Z-source network reduces input current ripple without relying on bulky electrolytic capacitors, which are prone to aging (as cited in capacitor aging studies). By minimizing ripple, the converter directly addresses the primary failure mode of fuel cell systems—capacitor aging and membrane degradation—thereby extending operational life.

How does the two-stage architecture with battery storage ensure DC bus voltage stability under grid faults?

The PEMFC exhibits a slow, second-level dynamic response, making it vulnerable to grid disturbances. The parallel battery energy storage (Vbat) on the DC bus provides rapid active power support, compensating for the fuel cell's sluggish response. This hybrid configuration maintains DC bus voltage within tight limits during load transients or grid faults, preventing overload and load shedding, as demonstrated by the system's ability to handle voltage fluctuations from 213 V to 323 V.

What are the scalability bottlenecks for high-power applications beyond 87.965 kW?

Scaling to higher power requires increasing the number of parallel phases or upgrading semiconductor ratings. The proposed three-phase parallel quasi-Z-source topology is modular; additional phases can be interleaved to further reduce ripple and share current. However, at higher power, the parasitic inductance and capacitance of the interleaved layout become critical, potentially affecting ripple cancellation. Thermal management of IGBTs and diodes also becomes challenging, necessitating advanced cooling solutions. The use of wide-bandgap devices (e.g., SiC) could mitigate these issues but at increased cost.

How does the cost of the proposed converter compare to conventional boost converters for fuel cell applications?

The proposed converter uses a quasi-Z-source network with six inductors, six capacitors, and six diodes per phase, plus three IGBTs, which is more complex than a single-stage boost converter. However, the reduced voltage stress on switches allows the use of lower-voltage-rated IGBTs, which are cheaper. Additionally, the elimination of large electrolytic capacitors (required in conventional designs for ripple suppression) reduces maintenance and replacement costs. The overall system cost may be comparable or lower when considering the extended fuel cell lifespan and improved reliability.

What empirical evidence supports the claimed low overcurrent risk in the proposed topology?

The three-phase parallel configuration divides the total input current (up to 700 A) equally among three phases, reducing per-phase switch current to approximately 233 A. This significantly lowers the overcurrent stress on individual IGBTs compared to a single-phase boost converter handling the full 700 A. The quasi-Z-source network also provides inherent shoot-through immunity, preventing catastrophic overcurrent events. Simulation and experimental results confirm stable operation under rated conditions, with switch currents well within safe operating limits.

Related Chinese Research & Cross-Citations

Research Citation2026
Wind Turbine Gearbox Fault Diagnosis Method Based on Improved CNN-XGBoost Fusion Model Under Gramian Angular Difference Field

Wind Turbine Gearbox Fault Diagnosis Method Based on Improved CNN-XGBoost Fusion Model Under Gramian Angular Difference Field

Gearbox failures account for 20–30% of total wind turbine faults and incur maintenance costs equivalent to 10–15% of overall turbine value. Conventional vibration diagnostic pipelines—complementary ensemble empirical mode decomposition with singular value energy spectrum, time-varying filtering empirical mode decomposition, and Teager energy spectrum analysis—remain bounded below 90% accuracy and depend on expert-driven feature engineering that is sensitive to non-stationary operating conditions and noise. This study proposes an intelligent diagnostic architecture that converts one-dimensional gearbox vibration signals into two-dimensional images via Gramian angular difference field (GADF) transformation, preserving intrinsic temporal correlation and time-frequency structure while exploiting matrix sparsity to suppress interference. An improved convolutional neural network (CNN) extracts multi-dimensional features: a convolutional block attention module (CBAM) is embedded in the convolutional layers to weight critical channels and focus on fault-sensitive spatial regions, and a modified βc-ACONC activation function replaces ReLU to mitigate neuron necrosis and enable selective activation. The extracted composite features are then fed into an XGBoost network whose hyperparameters are optimized by an improved sparrow search algorithm (ISSA). Validation on a laboratory wind turbine gearbox dataset yields diagnostic accuracy exceeding 99%, demonstrating robust fault identification capability under complex operating conditions.

Examine Full Data & PDF
Research Citation2026
Joint Forecasting of Wind and Photovoltaic Power Considering Complementarity

Joint Forecasting of Wind and Photovoltaic Power Considering Complementarity

The inherent spatiotemporal complementarity between wind and solar resources offers a theoretical basis for improving renewable power forecasting accuracy. This study proposes a joint wind-photovoltaic (PV) power forecasting strategy that explicitly exploits this complementarity. A bidirectional long short-term memory (BiLSTM) neural network serves as the baseline forecasting model, and a novel sorting and comparative optimization (SCO) algorithm is developed to optimize the model's hyperparameters. The SCO algorithm ranks individuals in ascending order and compares adjacent fitness values to escape local optima, a known deficiency in conventional metaheuristics such as genetic algorithms and particle swarm optimization. For wind farms and PV plants exhibiting significant complementarity, the joint forecasting strategy first aggregates their power outputs, normalizes the combined signal, and then feeds it into the optimized BiLSTM model. Experimental results demonstrate that the proposed SCO-BiLSTM model reduces the eMAPE by 10.313% compared with PSO-BiLSTM. Furthermore, joint forecasting under SCO-BiLSTM lowers the eRMSE by 27.443% relative to standalone PV power forecasting. The study also establishes that forecasting accuracy improves with stronger wind-solar complementarity but degrades as the forecasting horizon extends. These findings confirm that exploiting complementarity in joint forecasting substantially enhances predictive performance for renewable energy integration.

Examine Full Data & PDF
Research Citation2026
Ultra-Short-Term Wind Power Forecasting Based on Fluctuation Continuation Scenario Identification

Ultra-Short-Term Wind Power Forecasting Based on Fluctuation Continuation Scenario Identification

Existing ultra-short-term wind power forecasting methods exhibit limited performance due to insufficient extraction of fluctuation information and inadequate analysis of evolution patterns. This paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulence processes is investigated, and historical wind power dynamics are decoupled into a combination of nonlinear and linear fluctuation components. A fluctuation continuation concept is introduced, and the future continuation scale of wind power fluctuations is derived from nonlinear and linear decoupling parameters, thereby classifying fluctuation continuation scenarios. A sparse neural network (SNN) oriented to high-dimensional sparse features is constructed to identify historical fluctuation continuation scenarios, and ultra-short-term power forecasting is conducted separately for each scenario. Validation using measured wind speed and power data from three wind farms shows that, compared with baseline models, the proposed model improves root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by at least 1.46%, 2.44%, and 14.67%, respectively, demonstrating superior accuracy and stability. The method addresses the limitations of signal decomposition techniques that lack physical interpretability and are sensitive to hyperparameters, and overcomes the high-dimensional sparsity challenges faced by traditional scenario identification models.

Examine Full Data & PDF
Research Citation2026
Multi-Classifier Open Adversarial Network for Rolling Bearing Fault Diagnosis in Wind Turbine Generator Systems

Multi-Classifier Open Adversarial Network for Rolling Bearing Fault Diagnosis in Wind Turbine Generator Systems

Rolling bearings in wind turbine generator systems operate under variable speed and load conditions that induce significant data distribution shifts between training and field data, while unknown fault modes absent from the source domain are frequently misclassified as known classes. This study proposes a multi-classifier open adversarial network (MCOAN) for open-set fault diagnosis. Within an adversarial domain adaptation framework, a K-way classifier and an additional K+1-way one-vs-all classifier independently estimate target-sample similarity to the source domain. These similarity scores drive a dynamic weighting mechanism that adaptively reweights target samples during open-set adversarial training and supplies per-sample dynamic thresholds for known/unknown discrimination, thereby promoting cross-domain alignment of shared-class features while suppressing negative transfer from unknown samples. A non-adversarial domain classifier is introduced to stabilize dynamic weight estimation. Validation on two datasets demonstrates high-precision shared-class distribution alignment and unknown-class recognition with favorable robustness. The method removes reliance on empirically preset thresholds that plague conventional open-set back-propagation approaches, where a fixed threshold of 0.5 in the binary cross-entropy adversarial loss provides no per-sample adaptivity. By coupling K-way and K+1-way similarity estimates, MCOAN achieves simultaneous known-class alignment and unknown-class separation without prior knowledge of the unknown-class cardinality, addressing a persistent bottleneck in wind turbine drivetrain condition monitoring where unanticipated bearing failure modes emerge under field conditions.

Examine Full Data & PDF
Research Citation2026
Unsupervised Automated Identification Method for Abnormal States of Wind Turbine Gearboxes

Unsupervised Automated Identification Method for Abnormal States of Wind Turbine Gearboxes

Addressing the scarcity of labeled data for training classification models in wind turbine planetary gearbox anomaly identification, this study proposes an unsupervised automated detection method. Log Mel-band energy features are extracted from raw vibration signals and fed into an unsupervised anomaly recognition model centered on a U-net autoencoder. A health-state threshold is established based on reconstruction error between model input and output, enabling anomaly identification. The method is validated using factory gearbox test data and operational data from a wind farm in Yangtouya, Shanxi. For factory gearboxes, dual validation is performed using a spectrum amplitude modulation-based signal processing method. Results demonstrate that the proposed method achieves 93.34% recognition accuracy on both factory and wind farm test sets, confirming its capability to automatically and correctly separate abnormal wind turbine gearboxes. The approach eliminates reliance on labeled fault data, offering a scalable solution for full-lifecycle health monitoring, from factory acceptance testing to in-service early anomaly detection, adaptable across different operating conditions and turbine models.

Examine Full Data & PDF
Research Citation2026
Improved Adaptive Super-Twisting Sliding Mode Control for Permanent Magnet Synchronous Motors

Improved Adaptive Super-Twisting Sliding Mode Control for Permanent Magnet Synchronous Motors

Adaptive super-twisting sliding mode control (ASTSMC) for permanent magnet synchronous motors (PMSM) suffers from prolonged convergence and insufficient disturbance rejection under complex operating conditions. This paper proposes an improved ASTSMC incorporating a fixed-time disturbance observer (FTDO). A power term is introduced into the adaptive super-twisting controller to accelerate convergence far from the origin, while the discontinuous sign function is replaced by a continuous h(s) function to mitigate chattering. The FTDO ensures disturbance estimation converges within a fixed time independent of initial states, overcoming the limitations of traditional and finite-time observers. The estimated disturbance is fed forward to the sliding mode controller for compensation, enhancing robustness. The FTDO design is based on an auxiliary state variable z and its derivative, with error dynamics analyzed via Lyapunov stability. Comparative simulations against conventional disturbance observers and sliding mode controllers validate the proposed strategy. The results demonstrate shorter convergence time, improved dynamic performance, and superior disturbance rejection, making the approach suitable for high-performance servo systems. The method addresses the critical need for robust, fast-response control in electric vehicles, rail transit, and aerospace applications where PMSM drives face significant uncertainties and external disturbances.

Examine Full Data & PDF