Yazar "Siano, Pierluigi" seçeneğine göre listele

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    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-Inst Electrical Electronics Engineers Inc, 2025) Ozden, Mustafa; Ertekin, Davut; Siano, Pierluigi
    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.
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    The Design and Practical Realization of an Adaptable Grid Integrating Hydrogen Fuel Cell Setup With a Fuzzy-Logical Controller-Based SVPWM Boosted Inverter
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Ertekin, Davut; Baltaci, Kubra; Toprak, Muhammed Zeyd; Celebi, Mehmet; Ozden, Mustafa; Siano, Pierluigi
    The primary and fundamental requirement for a fuel cell (FC) stack is its reliable operation under various operating conditions. When FC stacks are used as the input voltage source with high ripple currents, the overall lifespan of the FC system decreases. Hence, power converter configurations need to minimize the current ripples originating from these sources. Additionally, the generated voltage from the FC stack is often lower than the required voltage level for grid connection. This paper presents a fuzzy logic controller (FLC)-equipped high-gain single-switched DC-DC boost converter. The proposed power converter topology utilizes an improved switched inductor and switched capacitor configuration to minimize input current ripples and enhance the voltage gain. The switched inductor cell is designed in such a way that its inductors charge and discharge simultaneously, effectively minimizing the input current ripple. Additionally, the proposed DC-DC boost converter utilizes a switched capacitor cell to double the generated voltage. The FLC offers real-time visualization and digital signal processing capabilities, and it is compatible with MATLAB software. For grid connection purposes, a space vector pulse width modulation (SVPWM)-based switching system is recommended, utilizing a full bridge inverter. The SVPWM technique is implemented by representing the desired output voltage with an equivalent vector VREF rotating counterclockwise, integrated with a digital signal processing (DSP)-based controller. The DSP microcontroller employed in this study operates at an 80 Mb/sec sampling speed and offers several advantages, including the ability to perform complex calculations, implement advanced control algorithms, and process signals in real-time. These capabilities contribute to enhanced performance, efficiency, and accuracy. Laboratory studies have been conducted to validate the accuracy and effectiveness of the theoretical investigations.