| Autor: |
Jhon Montano, Oscar Daniel Garzón, Andrés Alfonso Rosales Muñoz, L.F. Grisales-Noreña, Oscar Danilo Montoya |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Results in Engineering, Vol 16, Iss , Pp 100654- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2590-1230 |
| DOI: |
10.1016/j.rineng.2022.100654 |
| Popis: |
This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic Optimization Algorithm (AOA) and Successive Approximation (SA). This master-slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented; thus, the methodology explores solutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion Optimization (ALO), Black Hole Optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm Optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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