Acta Marisiensis.
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Anul 2024
Volum 21 (XXXVIII), nr 1 Volum 21 (XXXVIII), nr 2 Anul 2023 Volum 20 (XXXVII), nr 1 Volum 20 (XXXVII), nr 2 Anul 2022 Volum 19 (XXXVI), nr 1 Volum 19 (XXXVI), nr 2 Anul 2021 Volum 18 (XXXV), nr 1 Volum 18 (XXXV), nr 2 Anul 2020 Volum 17 (XXXIV), nr 1 Volum 17 (XXXIV), nr 2 Anul 2019 Volum 16 (XXXIII), nr 1 Volum 16 (XXXIII), nr 2 Anul 2018 Volum 15 (XXXII), nr 1 Volum 15 (XXXII), nr 2 Anul 2017 Volum 14 (XXXI), nr 1 Volum 14 (XXXI), nr 2 Anul 2016 Volum 13 (XXX), nr 1 Volum 13 (XXX), nr 2 Anul 2015 Volum 12 (XXIX), nr 1 Volum 12 (XXIX), nr 2 Anul 2014 Volum 11 (XXVIII), nr 1 Volum 11 (XXVIII), nr 2 Anul 2013 Volum 10 (XXVII), nr 1 Volum 10 (XXVII), nr 2 Anul 2012 Volum 9 (XXVI), nr 1 Volum 9 (XXVI), nr 2 Anul 2011 Volum 8 (XXV), nr 1 Volum 8 (XXV), nr 2 Anul 2010 Volum 7 (XXIV), nr 1 Volum 7 (XXIV), nr 2 Anul 2009 Volum 6 (XXIII) |
2022, Volume 19 (XXXVI), no 2
Sunday ADETONA, Michael JOHN, University of Lagos, Nigeria Salmar UMAR, Columbia University, New York, United States of America Abstract: The importance of reactive power to the economy and security of power systems cannot be overemphasized. For instance, Transmission losses increase when reactive power is unevenly distributed on transmission network; and power quality is affected as well. The cheapest way of reducing these transmission lines losses is via reactive power dispatch approach. This study therefore proposes an Improved Chaotic Particle Swarm Optimization algorithm (ICPSO) with the primary aim of reducing real power transmission line losses while adhering to system constraints. Although the traditional PSO has a fast convergence speed, it falls easily into local optimum and it is slow at the later stage of convergence. The ICPSO is proposed in this research to overcome these shortcomings. The approach combines PSO with chaotic map which increases particles’ diversity, allowing particles to explore the search region more; and a wingbeat frequency component which helps to sustain the rate of convergence of particles. MATPOWER 7.1 in MATLAB 2019a environment was utilized for the implementation. The purported algorithm was examined on IEEE14 and IEEE30 Test Beds respectively. When tried out on IEEE14 Test bed, real power loss was reduced from 13.393 MW to 12.260 MW; whereas real power transmission line loss was brought down from 17.557 MW to 15.977 MW when tried out on IEEE30 Test Bed. In terms of reducing real power transmission lines losses, the simulation results show that the proposed approach performs better when compared with other algorithms. DOI: https://doi.org/10.2478/amset-2022-0013 Pages: 20-29 Cite as: download info as bibtex View full article |
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Update: 18-Dec-2024 | © Published by University Press |