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An Evolutionary Algorithm-Based PWM Strategy for a Hybrid Power Converter

Author

Listed:
  • Alma Rodríguez

    (Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, Guadalajara Jalisco C.P. 44430, Mexico
    Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan Jalisco 45010, Mexico)

  • Avelina Alejo-Reyes

    (Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan Jalisco 45010, Mexico)

  • Erik Cuevas

    (Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, Guadalajara Jalisco C.P. 44430, Mexico)

  • Francisco Beltran-Carbajal

    (Departamento de Energía, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Mexico City C.P. 02200, Mexico)

  • Julio C. Rosas-Caro

    (Facultad de Ingeniería, Universidad Panamericana, Álvaro del Portillo 49, Zapopan Jalisco 45010, Mexico)

Abstract

In the past years, the interest in direct current to direct current converters has increased because of their application in renewable energy systems. Consequently, the research community is working on improving its efficiency in providing the required voltage to electronic devices with the lowest input current ripple. Recently, a hybrid converter which combines the boost and the Cuk converter in an interleaved manner has been introduced. The converter has the advantage of providing a relatively low input current ripple by a former strategy. However, it has been proposed to operate with dependent duty cycles, limiting its capacity to further decrease the input current ripple. Independent duty cycles can significantly reduce the input current ripple if the same voltage gain is achieved by an appropriate duty cycle combination. Nevertheless, finding the optimal duty cycle combination is not an easy task. Therefore, this article proposes a new pulse-width-modulation strategy for the hybrid interleaved boost-Cuk converter. The strategy includes the development of a novel mathematical model to describe the relationship between independent duty cycles and the input current ripple. The model is introduced to minimize the input current ripple by finding the optimal duty cycle combination using the differential evolution algorithm. It is shown that the proposed method further reduces the input current ripple for an operating range. Compared to the former strategy, the proposed method provides a more balanced power-sharing among converters.

Suggested Citation

  • Alma Rodríguez & Avelina Alejo-Reyes & Erik Cuevas & Francisco Beltran-Carbajal & Julio C. Rosas-Caro, 2020. "An Evolutionary Algorithm-Based PWM Strategy for a Hybrid Power Converter," Mathematics, MDPI, vol. 8(8), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1247-:d:392500
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    References listed on IDEAS

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    1. Zhao, Xuejing & Wang, Chen & Su, Jinxia & Wang, Jianzhou, 2019. "Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system," Renewable Energy, Elsevier, vol. 134(C), pages 681-697.
    2. Dulebenets, Maxim A., 2019. "A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility," International Journal of Production Economics, Elsevier, vol. 212(C), pages 236-258.
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