2022
DOI: 10.1049/gtd2.12560
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Transient stability constrained optimal power flow solution using ant colony optimization for continuous domains (ACO R )

Abstract: This paper aims to improve transient stability using the Ant Colony Optimization for Continuous Domains (ACOR). This improvement is obtained by solving the Transient Stability Constrained Optimal Power Flow (TSCOPF) problem and extracting the sensitivity coefficients. The presented costs minimization approach requires less execution time to manage energy resources efficiently and compared to other conventional methods, it also outperforms based on statistical indicators such as mean and standard deviation. Fur… Show more

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Cited by 6 publications
(3 citation statements)
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“…This mechanism was adapted to create the ACO algorithm for continuous function optimization [ 24 ]. More on the algorithm and its applications can be found, among others, in articles [ 25 , 26 , 27 , 28 ].…”
Section: Meta-heuristic Algorithmsmentioning
confidence: 99%
“…This mechanism was adapted to create the ACO algorithm for continuous function optimization [ 24 ]. More on the algorithm and its applications can be found, among others, in articles [ 25 , 26 , 27 , 28 ].…”
Section: Meta-heuristic Algorithmsmentioning
confidence: 99%
“…In [11], the cost, valve-point loading effect, loss, voltage deviation, and emission were optimised in the multiobjective OPF (MOOPF) problem and solved using a novel approach based on a modified and hybrid flower pollination algorithm to solve multi-objective optimal power flow (MHFPA). In [12], with cost minimisation as the major objective function, the OPF problem is solved using ant colony optimisation (ACO), considering transient stability as the major benefit to the power system. In a power system, reactive power plays a key role in maintaining the stability and security of the transmission system.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, new strategies that are backed by artificial intelligence (AI) have been made with the goal of fixing problems with analytical techniques. Artificial neural networks (ANN) [15], ant colony algorithm [16][17][18][19], fuzzy logic [20], multiobjective optimization [21,22], a flexible alternating current transmission system (FACTS) [23], quadratic programming [24], interior point optimization [25,26], and locational electricity [27] are some of the methods discussed. The fact that intelligent procedures may frequently adjust to a wide array of qualitative restrictions is the primary benefit offered by these methods.…”
mentioning
confidence: 99%