2020•Unpublished venueRequires access

Parameter Optimization of Differential Evolution and Particle Swarm Optimization in the Context of Optimal Power Flow

Tom Sennewald, Franz Linke, Jakob Reck, Dirk Westermann

Open publisher page 6 citations

Abstract

Optimization is playing a rising part in the operation of electrical power system. Metaheuristic optimization algorithms, such as Particle Swarm Optimization and Differential Evolution, are promising candidates to be applied to OPF problems. Their performance yet depends on the right parameter choice. This paper is meant to introduce a parameter optimization framework regarding the optimal parameter sets for both the Particle Swarm Optimization and Differential Evolution in the context of an optimal power flow.

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What this paper is about

Optimization is playing a rising part in the operation of electrical power system. Metaheuristic optimization algorithms, such as Particle Swarm Optimization and Differential Evolution, are promising candidates to be applied to OPF problems. Their performance yet depends on the right parameter choice. This paper is meant to introduce a parameter optimization framework regarding the optimal parameter sets for both the Particle Swarm Optimization and Differential Evolution in the context of an optimal power flow.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Optimization is playing a rising part in the operation of electrical power system. Metaheuristic optimization algorithms, such as Particle Swarm Optimization and Differential Evolution, are promising candidates to be applied to OPF problems. Their performance yet depends on the right parameter choice. This paper is meant to introduce a parameter optimization framework regarding the optimal parameter sets for both the Particle Swarm Optimization and Differential Evolution in the context of an optimal power flow.

Key concepts: Multi-swarm optimization, Metaheuristic, Differential evolution, Particle swarm optimization, Mathematical optimization, Meta-optimization, Context (archaeology), Power flow

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