IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i7p732-d525946.html
   My bibliography  Save this article

Comparison of Flux-Switching and Interior Permanent Magnet Synchronous Generators for Direct-Driven Wind Applications Based on Nelder–Mead Optimal Designing

Author

Listed:
  • Vladimir Prakht

    (Department of Electrical Engineering and Electric Technology Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Vladimir Dmitrievskii

    (Department of Electrical Engineering and Electric Technology Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Vadim Kazakbaev

    (Department of Electrical Engineering and Electric Technology Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Ekaterina Andriushchenko

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

The permanent magnet flux-switching machine (PMFSM) is one of the most promising machines with magnets inserted into the stator. To determine in which applications the use of PMFSM is promising, it is essential to compare the PMFSM with machines of other types. This study provides a theoretical comparison of the PMFSM with a conventional interior permanent magnet synchronous machine (IPMSM) in the gearless generator of a low-power wind turbine (332 rpm, 51.4 Nm). To provide a fair comparison, both machines are optimized using the Nelder–Mead algorithm. The minimized optimization objectives are the required power of frequency converter, cost of active materials, torque ripple and losses of a generator averaged over the working profile of the wind turbine. In order to reduce the computational time, the substituting profile method is applied. Based on the results of the calculations, the advantages and disadvantages of the considered machines were revealed: the IPMSM has significantly lower losses and higher efficiency than the PMFSM, and the PMFSM requires much less rare-earth magnets and copper and is, therefore, cheaper in mass production.

Suggested Citation

  • Vladimir Prakht & Vladimir Dmitrievskii & Vadim Kazakbaev & Ekaterina Andriushchenko, 2021. "Comparison of Flux-Switching and Interior Permanent Magnet Synchronous Generators for Direct-Driven Wind Applications Based on Nelder–Mead Optimal Designing," Mathematics, MDPI, vol. 9(7), pages 1-16, March.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:7:p:732-:d:525946
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/7/732/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/7/732/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charles Audet & Christophe Tribes, 2018. "Mesh-based Nelder–Mead algorithm for inequality constrained optimization," Computational Optimization and Applications, Springer, vol. 71(2), pages 331-352, November.
    2. Pishgar-Komleh, S.H. & Keyhani, A. & Sefeedpari, P., 2015. "Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 313-322.
    3. Vladimir Dmitrievskii & Vladimir Prakht & Vadim Kazakbaev, 2019. "Design Optimization of a Permanent-Magnet Flux-Switching Generator for Direct-Drive Wind Turbines," Energies, MDPI, vol. 12(19), pages 1-15, September.
    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. Vladimir Prakht & Vladimir Dmitrievskii & Vadim Kazakbaev, 2020. "Optimal Design of Gearless Flux-Switching Generator with Ferrite Permanent Magnets," Mathematics, MDPI, vol. 8(2), pages 1-14, February.
    2. Vladimir Dmitrievskii & Vladimir Prakht & Vadim Kazakbaev, 2019. "Design Optimization of a Permanent-Magnet Flux-Switching Generator for Direct-Drive Wind Turbines," Energies, MDPI, vol. 12(19), pages 1-15, September.
    3. Lingzhi Wang & Jun Liu & Fucai Qian, 2019. "A New Modeling Approach for the Probability Density Distribution Function of Wind power Fluctuation," Sustainability, MDPI, vol. 11(19), pages 1-16, October.
    4. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    5. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    6. Han, Qinkai & Wang, Tianyang & Chu, Fulei, 2022. "Nonparametric copula modeling of wind speed-wind shear for the assessment of height-dependent wind energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    7. Xu, Jin & Kanyingi, Peter Kairu & Wang, Keyou & Li, Guojie & Han, Bei & Jiang, Xiuchen, 2017. "Probabilistic small signal stability analysis with large scale integration of wind power considering dependence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1258-1270.
    8. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, vol. 9(5), pages 1-31, May.
    9. Saeed, Muhammad Abid & Ahmed, Zahoor & Zhang, Weidong, 2020. "Wind energy potential and economic analysis with a comparison of different methods for determining the optimal distribution parameters," Renewable Energy, Elsevier, vol. 161(C), pages 1092-1109.
    10. Youssef Kassem & Hüseyin Çamur & Ramzi Aateg Faraj Aateg, 2020. "Exploring Solar and Wind Energy as a Power Generation Source for Solving the Electricity Crisis in Libya," Energies, MDPI, vol. 13(14), pages 1-29, July.
    11. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    12. Miao, Shuwei & Yang, Hejun & Gu, Yingzhong, 2018. "A wind vector simulation model and its application to adequacy assessment," Energy, Elsevier, vol. 148(C), pages 324-340.
    13. Li, Delei & Geyer, Beate & Bisling, Peter, 2016. "A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea," Applied Energy, Elsevier, vol. 179(C), pages 575-589.
    14. Lidong Zhang & Qikai Li & Yuanjun Guo & Zhile Yang & Lei Zhang, 2018. "An Investigation of Wind Direction and Speed in a Featured Wind Farm Using Joint Probability Distribution Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    15. Yahya Z. Alharthi & Mahbube K. Siddiki & Ghulam M. Chaudhry, 2018. "Resource Assessment and Techno-Economic Analysis of a Grid-Connected Solar PV-Wind Hybrid System for Different Locations in Saudi Arabia," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
    16. Ebrahimi, Abbas & Movahhedi, Mohammadreza, 2018. "Wind turbine power improvement utilizing passive flow control with microtab," Energy, Elsevier, vol. 150(C), pages 575-582.
    17. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    18. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    19. Bagci, Kubra & Arslan, Talha & Celik, H. Eray, 2021. "Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    20. Zheng, Yi & You, Shi & Bindner, Henrik W. & Münster, Marie, 2022. "Optimal day-ahead dispatch of an alkaline electrolyser system concerning thermal–electric properties and state-transitional dynamics," Applied Energy, Elsevier, vol. 307(C).

    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:9:y:2021:i:7:p:732-:d:525946. 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.