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A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have i...

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Autores principales: Syah, Rahmad, Khorshidian Mianaei, Peyman, Elveny, Marischa, Ahmadian, Naeim, Ramdan, Dadan, Habibifar, Reza, Davarpanah, Afshin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347933/
https://www.ncbi.nlm.nih.gov/pubmed/34372483
http://dx.doi.org/10.3390/s21155246
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author Syah, Rahmad
Khorshidian Mianaei, Peyman
Elveny, Marischa
Ahmadian, Naeim
Ramdan, Dadan
Habibifar, Reza
Davarpanah, Afshin
author_facet Syah, Rahmad
Khorshidian Mianaei, Peyman
Elveny, Marischa
Ahmadian, Naeim
Ramdan, Dadan
Habibifar, Reza
Davarpanah, Afshin
author_sort Syah, Rahmad
collection PubMed
description The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.
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spelling pubmed-83479332021-08-08 A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources Syah, Rahmad Khorshidian Mianaei, Peyman Elveny, Marischa Ahmadian, Naeim Ramdan, Dadan Habibifar, Reza Davarpanah, Afshin Sensors (Basel) Article The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue. MDPI 2021-08-03 /pmc/articles/PMC8347933/ /pubmed/34372483 http://dx.doi.org/10.3390/s21155246 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Syah, Rahmad
Khorshidian Mianaei, Peyman
Elveny, Marischa
Ahmadian, Naeim
Ramdan, Dadan
Habibifar, Reza
Davarpanah, Afshin
A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
title A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
title_full A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
title_fullStr A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
title_full_unstemmed A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
title_short A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources
title_sort new hybrid algorithm for multi-objective reactive power planning via facts devices and renewable wind resources
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8347933/
https://www.ncbi.nlm.nih.gov/pubmed/34372483
http://dx.doi.org/10.3390/s21155246
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