Pre-Sizing of Power Converters Using Genetic Algorithms

Pre-Sizing of Power Converters Using Genetic Algorithms

This paper presents a computational tool to pre-size quickly and effectively static converters using genetic algorithms, with the objective function to minimize their volume, losses and cost. It was used as starting point an algorithm that performs the overall converter design as a function of the o...

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Bibliographic Details
Published in:Revista IEEE América Latina Vol. 16; no. 2; pp. 654 - 661
Main Authors: Carvalho Pivetta, Leticia, Confortin Sartori, Hamiltom, Pinheiro, Jose Renes
Format: Journal Article
Language:English
Published: Los Alamitos IEEE 01-02-2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Capacitors
Computational Tool
Data base management systems
Design
Design optimization
Efficiency
Electromagnetic interference
Genetic Algorithm
Genetic algorithms
IEEE transactions
Java
Power Converters
Software
Static power converters
Topology
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Summary:This paper presents a computational tool to pre-size quickly and effectively static converters using genetic algorithms, with the objective function to minimize their volume, losses and cost. It was used as starting point an algorithm that performs the overall converter design as a function of the operating point. The static converter design using genetic algorithms is made from your choice of topology, the input specification data, recommendations and standards and a database of components and devices. The language used for the development was JAVA 8 and the database management system was PostgreSQL. The results obtained by the genetic algorithm are compared to the values obtained by algorithms that performs complete scan of the search space, with respect to the quality of the solutions and the processing time to get results. The use of genetic algorithms helps a lot the process static converters designing, so that it quickly offers to designer different and optimized design options, considering the topology and input parameters.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2018.8327426