Design of a Three-Phase Shell-Type Distribution Transformer Using Evolutionary Algorithms

Juan Carlos Olivares-Galvan, Hector Ascencion-Mestiza, Serguei Maximov, Efrén Mezura-Montes and Rafael Escarela-Perez ()
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Juan Carlos Olivares-Galvan: Departamento de Energia, Universidad Autonoma Metropolitana, Ciudad de Mexico 02128, Mexico
Hector Ascencion-Mestiza: PGIIE, Tecnológico Nacional de México, Campus Morelia, Av. Tecnológico No. 1500, Lomas de Santiaguito, Morelia 58120, Mexico
Serguei Maximov: PGIIE, Tecnológico Nacional de México, Campus Morelia, Av. Tecnológico No. 1500, Lomas de Santiaguito, Morelia 58120, Mexico
Efrén Mezura-Montes: Artificial Intelligence Research Institute, Universidad Veracruzana, Xalapa 91097, Mexico
Rafael Escarela-Perez: Departamento de Energia, Universidad Autonoma Metropolitana, Ciudad de Mexico 02128, Mexico

Energies, 2023, vol. 16, issue 10, 1-16

Abstract: In this paper, three metaheuristic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) are compared in terms of minimizing the total owning cost (TOC) of the active part of a three-phase shell-type distribution transformer. The three methods use six inputs: power rating, primary voltage, secondary voltage, primary and secondary winding connections, and frequency. The TOC of the transformer, which includes the cost of the basic materials of the transformer plus the cost of losses, is minimized under the imposed constraints (excitation current, impedance, no-load losses, load losses, and efficiency) usually specified in the standards. As a case study, the three algorithms are applied to optimize the design of a three-phase shell-type distribution transformer of 750 kVA. All applied metaheuristic algorithms provide good results, while DE avoids local optima leading to better TOC reduction. The results of the optimization algorithms used are superior to those of the manufacturer, showing a 6% TOC reduction. Optimization of the design of a power transformer may have important implications for reducing greenhouse gas emissions and extending the lifetime of the equipment.

Keywords: evolutionary algorithms; genetic algorithm; particle swarm optimization; differential evolution; total owning cost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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