Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems
A crucial aspect of DC-DC converters employed in renewable energy sources such as fuel cells is their ability to exhibit substantial increases in DC voltage and maintain an efficient structure while minimizing input current ripple. These factors play a pivotal role in enhancing the longevity of thes...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10818659/ |
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| author | Mustafa Ozden Davut Ertekin Pierluigi Siano |
| author_facet | Mustafa Ozden Davut Ertekin Pierluigi Siano |
| author_sort | Mustafa Ozden |
| collection | DOAJ |
| description | A crucial aspect of DC-DC converters employed in renewable energy sources such as fuel cells is their ability to exhibit substantial increases in DC voltage and maintain an efficient structure while minimizing input current ripple. These factors play a pivotal role in enhancing the longevity of these energy sources and ensuring their compatibility with high-voltage AC and DC grids. This study introduces a high-gain DC-DC step-up converter that incorporates a continuous input current cell and a switched capacitor voltage-boosting output cell to address these requirements. The control process of this proposed converter is executed using an artificial neural network based on the Levenberg-Marquardt learning algorithm. The primary difference in this research lies in obtaining the artificial neural network-based controller directly from the circuit’s characteristic equations, rather than generating it through another controller. A feedback control strategy has been formulated, where the artificial neural network consistently produces duty increment values based on the reference voltage. Additionally, the network’s input includes not only the reference signal but also the circuit input voltage and output current value. As a result, the stability of the circuit’s output voltage is maintained against variations in input voltage and load changes. A laboratory-designed workbench underwent testing, and the experimental results substantiated the theoretical inquiries and simulation outcomes. |
| format | Article |
| id | doaj-art-87e01a9c343d4efda71e2ab54814960b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-87e01a9c343d4efda71e2ab54814960b2025-08-20T02:41:03ZengIEEEIEEE Access2169-35362025-01-01133613363110.1109/ACCESS.2024.352437810818659Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy SystemsMustafa Ozden0https://orcid.org/0000-0002-0362-4017Davut Ertekin1https://orcid.org/0000-0003-2234-3453Pierluigi Siano2https://orcid.org/0000-0002-0975-0241Electrical and Electronics Engineering Department, Bursa Technical University, Yıldırım, Bursa, TürkiyePower Electronics Center of Electrical and Electronics Engineering Research Laboratories, Bursa Technical University, Yıldırım, Bursa, TürkiyeDepartment of Management and Innovation Systems, University of Salerno, Fisciano, ItalyA crucial aspect of DC-DC converters employed in renewable energy sources such as fuel cells is their ability to exhibit substantial increases in DC voltage and maintain an efficient structure while minimizing input current ripple. These factors play a pivotal role in enhancing the longevity of these energy sources and ensuring their compatibility with high-voltage AC and DC grids. This study introduces a high-gain DC-DC step-up converter that incorporates a continuous input current cell and a switched capacitor voltage-boosting output cell to address these requirements. The control process of this proposed converter is executed using an artificial neural network based on the Levenberg-Marquardt learning algorithm. The primary difference in this research lies in obtaining the artificial neural network-based controller directly from the circuit’s characteristic equations, rather than generating it through another controller. A feedback control strategy has been formulated, where the artificial neural network consistently produces duty increment values based on the reference voltage. Additionally, the network’s input includes not only the reference signal but also the circuit input voltage and output current value. As a result, the stability of the circuit’s output voltage is maintained against variations in input voltage and load changes. A laboratory-designed workbench underwent testing, and the experimental results substantiated the theoretical inquiries and simulation outcomes.https://ieeexplore.ieee.org/document/10818659/Fuel cellsmart controlartificial neural networkgrid-connected power converterlow-input current cellhigh-voltage gain cell |
| spellingShingle | Mustafa Ozden Davut Ertekin Pierluigi Siano Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems IEEE Access Fuel cell smart control artificial neural network grid-connected power converter low-input current cell high-voltage gain cell |
| title | Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems |
| title_full | Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems |
| title_fullStr | Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems |
| title_full_unstemmed | Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems |
| title_short | Levenberg-Marquardt Algorithm-Based Neural Network Smart Control Strategy for a Low-Input Current Ripple and High-Voltage Gain Power Converter in Fuel-Cells Energy Systems |
| title_sort | levenberg marquardt algorithm based neural network smart control strategy for a low input current ripple and high voltage gain power converter in fuel cells energy systems |
| topic | Fuel cell smart control artificial neural network grid-connected power converter low-input current cell high-voltage gain cell |
| url | https://ieeexplore.ieee.org/document/10818659/ |
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