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              <text>Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks</text>
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              <text>Artificial neural network; DC- approximate dynamic programming; DC-DC and DC-AC; Universal operation</text>
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              <text>This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.  The Author(s) 2024.</text>
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              <text>Suresh K.; Parimalasundar E.; Kumar B.H.; Singh A.R.; Bajaj M.; Tuka M.B.</text>
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              <text>Scientific Reports, Vol-14, No. 1</text>
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              <text>Nature Research</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1038/s41598-024-71916-z" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1038/s41598-024-71916-z&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203277639&amp;amp;doi=10.1038%2Fs41598-024-71916-z&amp;amp;partnerID=40&amp;amp;md5=1a8e50606c0a690a49b808e2a4cfc89e" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203277639&amp;amp;doi=10.1038%2fs41598-024-71916-z&amp;amp;partnerID=40&amp;amp;md5=1a8e50606c0a690a49b808e2a4cfc89e&lt;/a&gt;</text>
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 20452322; PubMed ID: 39245750</text>
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              <text>Suresh K., Department of Electrical and Electronics Engineering, Christ Deemed to Be University, Bangalore, India; Parimalasundar E., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Kumar B.H., Department of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India; Singh A.R., Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Hubei Shiyan, 442000, China; Bajaj M., Department of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan, Graphic Era Hill University, Dehradun, 248002, India; Tuka M.B., Department of Electrical and Computer Engineering, College of Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia</text>
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