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Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power
Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this researc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492785/ https://www.ncbi.nlm.nih.gov/pubmed/37689709 http://dx.doi.org/10.1038/s41598-023-42155-5 |
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author | Kumar, Prince Kumar, Kunal Kumar Bohre, Aashish Adhikary, Nabanita Lakew Tesfaye, Eshet |
author_facet | Kumar, Prince Kumar, Kunal Kumar Bohre, Aashish Adhikary, Nabanita Lakew Tesfaye, Eshet |
author_sort | Kumar, Prince |
collection | PubMed |
description | Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/SIMULINK environment. |
format | Online Article Text |
id | pubmed-10492785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104927852023-09-11 Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power Kumar, Prince Kumar, Kunal Kumar Bohre, Aashish Adhikary, Nabanita Lakew Tesfaye, Eshet Sci Rep Article Increased innovation on finding new ways to generate energy from different sources to meet the growing demand of consumers has led to various challenges in controlling the power network when it faces different disruptions. To address these challenges, a new approach has been proposed in this research paper, which combines a controller with a soft computing technique called Particle Swarm Optimization (PSO). The study considers a power system with four units, where three different energy sources are utilized and distributed across two areas. Each area has two power sources, with one area having a combination of thermal and gas power plants, and the other area consisting of a nuclear power plant and a gas power plant. Transmitting power from the nuclear power plant is particularly complex due to its high sensitivity to disturbances. Therefore, an intelligent and efficient controller is needed to ensure robust control in this type of power network that includes nuclear power. The paper also conducts a thorough analysis of the harmful emissions associated with electricity generation from the different power plants considered. The goal is to reduce the carbon footprint associated with power generation. The proposed work and analysis in the paper are implemented using the MATLAB/SIMULINK environment. Nature Publishing Group UK 2023-09-09 /pmc/articles/PMC10492785/ /pubmed/37689709 http://dx.doi.org/10.1038/s41598-023-42155-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kumar, Prince Kumar, Kunal Kumar Bohre, Aashish Adhikary, Nabanita Lakew Tesfaye, Eshet Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
title | Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
title_full | Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
title_fullStr | Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
title_full_unstemmed | Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
title_short | Intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
title_sort | intelligent planning of controllers for improved resilience in multi-area system involving nuclear power |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492785/ https://www.ncbi.nlm.nih.gov/pubmed/37689709 http://dx.doi.org/10.1038/s41598-023-42155-5 |
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