IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i8p1247-d392500.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/8/1247/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/8/8/1247/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng-Long Wei & Gai-Ge Wang, 2020. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization," Mathematics, MDPI, vol. 8(9), pages 1-23, August.
    2. Mohammad Amin Amani & Mohammad Mahdi Nasiri, 2023. "A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach," Journal of Combinatorial Optimization, Springer, vol. 45(5), pages 1-32, July.
    3. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    4. Bingtao Quan & Sujian Li & Kuo-Jui Wu, 2022. "Optimizing the Vehicle Scheduling Problem for Just-in-Time Delivery Considering Carbon Emissions and Atmospheric Particulate Matter," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    5. Md Tariqul Islam & M. J. Hossain, 2023. "Artificial Intelligence for Hosting Capacity Analysis: A Systematic Literature Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    6. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    7. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.
    8. Yunes Almansoub & Ming Zhong & Asif Raza & Muhammad Safdar & Abdelghani Dahou & Mohammed A. A. Al-qaness, 2022. "Exploring the Effects of Transportation Supply on Mixed Land-Use at the Parcel Level," Land, MDPI, vol. 11(6), pages 1-28, May.
    9. Ibrahim Mohamed Diaaeldin & Mahmoud A. Attia & Amr K. Khamees & Othman A. M. Omar & Ahmed O. Badr, 2023. "A Novel Multiobjective Formulation for Optimal Wind Speed Modeling via a Mixture Probability Density Function," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    10. Jiamin Wei & YangQuan Chen & Yongguang Yu & Yuquan Chen, 2019. "Optimal Randomness in Swarm-Based Search," Mathematics, MDPI, vol. 7(9), pages 1-19, September.
    11. Oluwatosin Theophilus & Maxim A. Dulebenets & Junayed Pasha & Olumide F. Abioye & Masoud Kavoosi, 2019. "Truck Scheduling at Cross-Docking Terminals: A Follow-Up State-Of-The-Art Review," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    12. Li, Lei & Yin, Xiao-Li & Jia, Xin-Chun & Sobhani, Behrooz, 2020. "Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm," Energy, Elsevier, vol. 192(C).
    13. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    14. Hubert Ruta & Tomasz Krakowski & Paweł Lonkwic, 2022. "Optimisation of the Magnetic Circuit of a Measuring Head for Diagnostics of Steel-Polyurethane Load-Carrying Belts Using Numerical Methods," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    15. Yupeng Zhou & Jinshu Li & Yang Liu & Shuai Lv & Yong Lai & Jianan Wang, 2020. "Improved Memetic Algorithm for Solving the Minimum Weight Vertex Independent Dominating Set," Mathematics, MDPI, vol. 8(7), pages 1-17, July.
    16. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    17. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    18. Gao, Yang & Ma, Shaoxiu & Wang, Tao & Miao, Changhong & Yang, Fan, 2022. "Distributed onshore wind farm siting using intelligent optimization algorithm based on spatial and temporal variability of wind energy," Energy, Elsevier, vol. 258(C).
    19. Byungjun Ju & Minsu Kim & Ilkyeong Moon, 2021. "Vehicle Routing Problem Considering Reconnaissance and Transportation," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    20. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1247-:d:392500. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.