Proceedings of the International Conference on Advanced Technology and Multidiscipline (ICATAM 2024)

Solving the Problem of Optimal Reactive Power Dispatch Using Physical-Inspired Metaheuristic Algorithm

Authors
Prisma Megantoro1, 2, *, Syahirah Abd Halim1, Nor Azwan Mohd Kamari1, Lilik Jamilatul Awalin1, 2
1Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
2Faculty of Advanced Technology and Multidicipline, Universitas Airlangga, Surabaya, Indonesia
*Corresponding author. Email: prisma.megantoro@ftmm.unair.ac.id
Corresponding Author
Prisma Megantoro
Available Online 1 November 2024.
DOI
10.2991/978-94-6463-566-9_4How to use a DOI?
Keywords
Archimedes optimization algorithm; meta-heuristic optimization; optimal reactive power dispatch; power system; transmission network
Abstract

One of the methods for improving the efficiency of power system transmission is the Optimal Reactive Power Dispatch (ORPD). The ORPD technique has been used to minimize the transmission network’s power loss by regulating the system control variables. The control variables regulated by ORPD control the reactive power generated sources, such as generator voltage, tap ratio setting for transformers, and reactive power injected by VAR compensator devices. On the other hand, ORPD is a non-linear and non-convex problem, so it needs an optimizer algorithm capable of solving its characteristics. This article discusses using a meta-heuristic algorithm (MA) to solve the ORPD problem. The MA used is one kind of physical phenomena-inspired optimizer called Archimedes optimization algorithm (AOA). The AOA tracks the best combination of all control variables, producing the maximum total active power loss. The performance of AOA tracking used in ORPD problem solving is tested using a standard IEEE 30 bus system. The AOA is compared to other MAs in comparison analysis to perform its superiority. AOA was also tested in correlation analysis to determine the best value of population size and the maximum number of iterations based-on its tracking accuracy, speed of convergence, and processing time. The results show that ORPD problem-solving with AOA has the advantage of reducing power loss by 11.9%. The simulated results confirm the efficiency and robustness of AOA for solving the ORPD problem.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advanced Technology and Multidiscipline (ICATAM 2024)
Series
Advances in Engineering Research
Publication Date
1 November 2024
ISBN
978-94-6463-566-9
ISSN
2352-5401
DOI
10.2991/978-94-6463-566-9_4How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Prisma Megantoro
AU  - Syahirah Abd Halim
AU  - Nor Azwan Mohd Kamari
AU  - Lilik Jamilatul Awalin
PY  - 2024
DA  - 2024/11/01
TI  - Solving the Problem of Optimal Reactive Power Dispatch Using Physical-Inspired Metaheuristic Algorithm
BT  - Proceedings of the  International Conference on Advanced Technology and Multidiscipline (ICATAM 2024)
PB  - Atlantis Press
SP  - 34
EP  - 53
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-566-9_4
DO  - 10.2991/978-94-6463-566-9_4
ID  - Megantoro2024
ER  -