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Design of fractional evolutionary processing for reactive power planning with FACTS devices
Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap se...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804412/ https://www.ncbi.nlm.nih.gov/pubmed/33436809 http://dx.doi.org/10.1038/s41598-020-79838-2 |
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author | Muhammad, Yasir Akhtar, Rizwan Khan, Rahimdad Ullah, Farman Raja, Muhammad Asif Zahoor Machado, J. A. Tenreiro |
author_facet | Muhammad, Yasir Akhtar, Rizwan Khan, Rahimdad Ullah, Farman Raja, Muhammad Asif Zahoor Machado, J. A. Tenreiro |
author_sort | Muhammad, Yasir |
collection | PubMed |
description | Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap settings, generator voltages and reactive power sources, such as flexible alternating current transmission systems (FACTS). The optimal dispatch improves the system security, voltage profile, power transfer capability and overall network efficiency. In the present work, a fractional evolutionary approach achieves the desired objectives of reactive power planning by incorporating FACTS devices. Two compensation arrangements are possible: the shunt type compensation, through Static Var compensator (SVC) and the series compensation through the Thyristor controlled series compensator (TCSC). The fractional order Darwinian Particle Swarm Optimization (FO-DPSO) is implemented on the standard IEEE 30, IEEE 57 and IEEE 118 bus test systems. The power flow analysis is used for determining the location of TCSC, while the voltage collapse proximity indication (VCPI) method identifies the location of the SVC. The superiority of the FO-DPSO is demonstrated by comparing the results with those obtained by other techniques in terms of measure of central tendency, variation indices and time complexity. |
format | Online Article Text |
id | pubmed-7804412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78044122021-01-13 Design of fractional evolutionary processing for reactive power planning with FACTS devices Muhammad, Yasir Akhtar, Rizwan Khan, Rahimdad Ullah, Farman Raja, Muhammad Asif Zahoor Machado, J. A. Tenreiro Sci Rep Article Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap settings, generator voltages and reactive power sources, such as flexible alternating current transmission systems (FACTS). The optimal dispatch improves the system security, voltage profile, power transfer capability and overall network efficiency. In the present work, a fractional evolutionary approach achieves the desired objectives of reactive power planning by incorporating FACTS devices. Two compensation arrangements are possible: the shunt type compensation, through Static Var compensator (SVC) and the series compensation through the Thyristor controlled series compensator (TCSC). The fractional order Darwinian Particle Swarm Optimization (FO-DPSO) is implemented on the standard IEEE 30, IEEE 57 and IEEE 118 bus test systems. The power flow analysis is used for determining the location of TCSC, while the voltage collapse proximity indication (VCPI) method identifies the location of the SVC. The superiority of the FO-DPSO is demonstrated by comparing the results with those obtained by other techniques in terms of measure of central tendency, variation indices and time complexity. Nature Publishing Group UK 2021-01-12 /pmc/articles/PMC7804412/ /pubmed/33436809 http://dx.doi.org/10.1038/s41598-020-79838-2 Text en © The Author(s) 2021 Open AccessThis 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/. |
spellingShingle | Article Muhammad, Yasir Akhtar, Rizwan Khan, Rahimdad Ullah, Farman Raja, Muhammad Asif Zahoor Machado, J. A. Tenreiro Design of fractional evolutionary processing for reactive power planning with FACTS devices |
title | Design of fractional evolutionary processing for reactive power planning with FACTS devices |
title_full | Design of fractional evolutionary processing for reactive power planning with FACTS devices |
title_fullStr | Design of fractional evolutionary processing for reactive power planning with FACTS devices |
title_full_unstemmed | Design of fractional evolutionary processing for reactive power planning with FACTS devices |
title_short | Design of fractional evolutionary processing for reactive power planning with FACTS devices |
title_sort | design of fractional evolutionary processing for reactive power planning with facts devices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7804412/ https://www.ncbi.nlm.nih.gov/pubmed/33436809 http://dx.doi.org/10.1038/s41598-020-79838-2 |
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