2020
DOI: 10.1007/s12652-020-02142-4
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Comparative study of Pareto optimal multi objective cuckoo search algorithm and multi objective particle swarm optimization for power loss minimization incorporating UPFC

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Cited by 15 publications
(6 citation statements)
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“…The algorithm aims to obtain the Pareto Optimal with respect to some switch changes and power loss with the highest voltage stability limit. This algorithm is improved for Pareto Optimal from the MOCS algorithm in [18]. The algorithm steps are:…”
Section: Proposed Algorithm and Pareto Optimalmentioning
confidence: 99%
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“…The algorithm aims to obtain the Pareto Optimal with respect to some switch changes and power loss with the highest voltage stability limit. This algorithm is improved for Pareto Optimal from the MOCS algorithm in [18]. The algorithm steps are:…”
Section: Proposed Algorithm and Pareto Optimalmentioning
confidence: 99%
“…MOCS is the extended version of the cuckoo search algorithm developed by Yang and Deb [14]. It has been tested against relevant test functions and then successfully applied to numerous problems [15][16][17][18]. This study uses MOCS to solve the DNR optimization problem focusing on power loss reduction and switching operations.…”
Section: Introductionmentioning
confidence: 99%
“…Minimizing power losses is one of the primary goals of installing FACTS devices in power grids; therefore, almost all articles dealing with these devices have addressed this issue [5]- [9]. Numerous authors have looked into the advantages of UPFC placement on system performance.…”
Section: Introductionmentioning
confidence: 99%
“…However, the problem of integrating these into the power system is quite complicated where achieving maximum effectiveness is through proper sizing and siting of SVCs. For this reason, many authors have tried to resolve this problem by using various optimization techniques, such as differential evolution (DE) [3], whale optimization algorithm (WOA) [4], simulated annealing (SA), and particle swarm optimization (PSO) [5], multi-objective genetic algorithm (MOGA) [6], multi-objective cuckoo search (MOCS) [7], imperialistic competitive algorithm (ICA) [3], harmony search (HS) [8].…”
Section: Introductionmentioning
confidence: 99%