Evidence map›Paper›PMID 39245750›Full record

ArticleScientific reports2024

Design and implementation of a universal converter for microgrid applications using approximate dynamic programming and artificial neural networks.

K Suresh, E Parimalasundar, B Hemanth Kumar, Arvind R Singh, Mohit Bajaj, Milkias Berhanu Tuka

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

K SureshDepartment of Electrical and Electronics Engineering, Christ Deemed to Be University, Bangalore, India.
E ParimalasundarDepartment of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India.
B Hemanth KumarDepartment of Electrical and Electronics Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India.
Arvind R SinghDepartment of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Hubei Shiyan, 442000, People's Republic of China. arvindsinghwce@gmail.com.
Mohit BajajDepartment of Electrical Engineering, Graphic Era (Deemed to Be University), Dehradun, 248002, India. mb.czechia@gmail.com.
Milkias Berhanu TukaDepartment of Electrical and Computer Engineering, College of Engineering, Sustainable Energy Center of Excellence, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia. milkias.berhanu@aastu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial neural networkDC- approximate dynamic programmingDC-DC and DC-ACUniversal operation

Identifiers

PMID39245750
PMCPMC11381510

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.