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Comparative Analysis of Genetic Algorithm and Particle Swarm Optimization for Optimal Power Flow on IEEE 30-Bus System
The intricate operation of an electrical power system involving nonlinearity and computational challenges has made it a highly sought-after field of study and research. This paper aims to resolve one particular optimal power flow problem using metaheuristics optimization techniques, namely Particle swarm optimization, to arrive at improved results compared to previous studies. Optimal power flow can be considered a non-linear, nonconvex complex optimization problem, and as such, metaheuristics search is typically suited to obtain an optimum or near-optimum solution. The adopted approach's benefit is finding a solution that reduces fuel costs while keeping generator power outputs, bus voltages, shunt capacitors and reactors, and transformer tap-setting within safe limits. The paper provides a comparative analysis of the genetic algorithm and particle swarm optimization to address the problem of intricacy. It shows the numerical advantages of the adopted solution compared to other genetic algorithms and artificial bee colonies algorithms. The outcomes of this research paper suggest a methodology capable of realizing substantial savings, potentially exceeding ∃5 per kilowatt-hour (KWH). Therefore, this research offers an innovative and economically advantageous solution with high potential for future power industry applications.
Comparative Analysis of Genetic Algorithm and Particle Swarm Optimization for Optimal Power Flow on IEEE 30-Bus System
The intricate operation of an electrical power system involving nonlinearity and computational challenges has made it a highly sought-after field of study and research. This paper aims to resolve one particular optimal power flow problem using metaheuristics optimization techniques, namely Particle swarm optimization, to arrive at improved results compared to previous studies. Optimal power flow can be considered a non-linear, nonconvex complex optimization problem, and as such, metaheuristics search is typically suited to obtain an optimum or near-optimum solution. The adopted approach's benefit is finding a solution that reduces fuel costs while keeping generator power outputs, bus voltages, shunt capacitors and reactors, and transformer tap-setting within safe limits. The paper provides a comparative analysis of the genetic algorithm and particle swarm optimization to address the problem of intricacy. It shows the numerical advantages of the adopted solution compared to other genetic algorithms and artificial bee colonies algorithms. The outcomes of this research paper suggest a methodology capable of realizing substantial savings, potentially exceeding ∃5 per kilowatt-hour (KWH). Therefore, this research offers an innovative and economically advantageous solution with high potential for future power industry applications.
Comparative Analysis of Genetic Algorithm and Particle Swarm Optimization for Optimal Power Flow on IEEE 30-Bus System
Halwani, Said (Autor:in) / Tawfik, Hissam (Autor:in) / Dahrouj, Hayssam (Autor:in) / Elnady, A. (Autor:in)
03.06.2024
660168 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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