Issue |
E3S Web Conf.
Volume 516, 2024
10th Conference on Emerging Energy and Process Technology (CONCEPT 2023)
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Article Number | 05003 | |
Number of page(s) | 6 | |
Section | Electronic and Electrical Engineering | |
DOI | https://doi.org/10.1051/e3sconf/202451605003 | |
Published online | 15 April 2024 |
Power loss minimization by optimal allocation and sizing of STATCOM via particle swarm optimization
1 Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
2 Centre of Electrical Energy System (CEES), Department of Electrical Power Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia
3 Fakulti Technical and Engineering Education, School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
4 Department of Electrical Engineering, State University of Malang, Malang, Indonesia
* Corresponding author: norzanah@utm.my
The provision of electricity is problematic in remote and rural areas. The distribution system is insufficiently efficient to provide rural areas with electricity. Long distance between the power supply and rural areas is the reason why there is insufficient electricity. Static synchronous compensator (STATCOM) technology is one of the solutions that have more positive effects, such as reducing power losses, and can therefore increase the system’s efficacy. However, the non-optimal allocation and magnitude of STATCOM may increase losses and have a negative impact on power, resulting in a decrease in system efficiency. This paper introduces the Particle Swarm Optimization (PSO) algorithm as the proposed algorithm to optimize the location and capacity of STATCOM in order to reduce power losses in the distribution system. The proposed technique is implemented on a 15-bus IEEE system and simulated using the MATLAB program. The simulation results demonstrate that the optimal location and quantity of the STATCOM proposed by PSO effectively reduces power loss by 6.058% compared to without PSO implementation.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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