IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1498-d513451.html
   My bibliography  Save this article

Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power

Author

Listed:
  • Maurizio Fantauzzi

    (Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy)

  • Davide Lauria

    (Department of Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy)

  • Fabio Mottola

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy)

  • Daniela Proto

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy)

Abstract

This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime.

Suggested Citation

  • Maurizio Fantauzzi & Davide Lauria & Fabio Mottola & Daniela Proto, 2021. "Estimating Wind Farm Transformers Rating through Lifetime Characterization Based on Stochastic Modeling of Wind Power," Energies, MDPI, vol. 14(5), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1498-:d:513451
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1498/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/5/1498/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grzegorz Dombek & Zbigniew Nadolny & Piotr Przybylek & Radoslaw Lopatkiewicz & Agnieszka Marcinkowska & Lukasz Druzynski & Tomasz Boczar & Andrzej Tomczewski, 2020. "Effect of Moisture on the Thermal Conductivity of Cellulose and Aramid Paper Impregnated with Various Dielectric Liquids," Energies, MDPI, vol. 13(17), pages 1-17, August.
    2. Verdejo, Humberto & Awerkin, Almendra & Saavedra, Eugenio & Kliemann, Wolfgang & Vargas, Luis, 2016. "Stochastic modeling to represent wind power generation and demand in electric power system based on real data," Applied Energy, Elsevier, vol. 173(C), pages 283-295.
    3. Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy," Papers 1811.09312, arXiv.org, revised Jul 2022.
    4. Saifal Talpur & Tek Tjing Lie & Ramon Zamora & Bhaba Priyo Das, 2020. "Maximum Utilization of Dynamic Rating Operated Distribution Transformer (DRoDT) with Battery Energy Storage System: Analysis on Impact from Battery Electric Vehicles Charging," Energies, MDPI, vol. 13(13), pages 1-21, July.
    5. Yazdani-Asrami, Mohammad & Mirzaie, Mohammad & Shayegani Akmal, Amir Abbas, 2013. "No-load loss calculation of distribution transformers supplied by nonsinusoidal voltage using three-dimensional finite element analysis," Energy, Elsevier, vol. 50(C), pages 205-219.
    6. Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Effect of Loads and Other Key Factors on Oil-Transformer Ageing: Sustainability Benefits and Challenges," Energies, MDPI, vol. 8(10), pages 1-40, October.
    7. Altunkaynak, Abdüsselam & Erdik, Tarkan & Dabanlı, İsmail & Şen, Zekai, 2012. "Theoretical derivation of wind power probability distribution function and applications," Applied Energy, Elsevier, vol. 92(C), pages 809-814.
    8. Chiodo, Elio & Lauria, Davide & Mottola, Fabio & Pisani, Cosimo, 2016. "Lifetime characterization via lognormal distribution of transformers in smart grids: Design optimization," Applied Energy, Elsevier, vol. 177(C), pages 127-135.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohamed Zaidan Qawaqzeh & Oleksandr Miroshnyk & Taras Shchur & Robert Kasner & Adam Idzikowski & Weronika Kruszelnicka & Andrzej Tomporowski & Patrycja Bałdowska-Witos & Józef Flizikowski & Marcin Zaw, 2021. "Research of Emergency Modes of Wind Power Plants Using Computer Simulation," Energies, MDPI, vol. 14(16), pages 1-15, August.

    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. Alvaro Carreno & Marcelo Perez & Carlos Baier & Alex Huang & Sanjay Rajendran & Mariusz Malinowski, 2021. "Configurations, Power Topologies and Applications of Hybrid Distribution Transformers," Energies, MDPI, vol. 14(5), pages 1-35, February.
    2. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    3. Miro Antonijević & Stjepan Sučić & Hrvoje Keserica, 2018. "Augmented Reality Applications for Substation Management by Utilizing Standards-Compliant SCADA Communication," Energies, MDPI, vol. 11(3), pages 1-17, March.
    4. Munir Ali Elfarra & Mustafa Kaya, 2018. "Comparison of Optimum Spline-Based Probability Density Functions to Parametric Distributions for the Wind Speed Data in Terms of Annual Energy Production," Energies, MDPI, vol. 11(11), pages 1-15, November.
    5. Busiswe Skosana & Mukwanga W. Siti & Nsilulu T. Mbungu & Sonu Kumar & Willy Mulumba, 2023. "An Evaluation of Potential Strategies in Renewable Energy Systems and Their Importance for South Africa—A Review," Energies, MDPI, vol. 16(22), pages 1-27, November.
    6. Pawel Rozga & Abderrahmane Beroual & Piotr Przybylek & Maciej Jaroszewski & Konrad Strzelecki, 2020. "A Review on Synthetic Ester Liquids for Transformer Applications," Energies, MDPI, vol. 13(23), pages 1-33, December.
    7. Jia, Xiaoyu & Lin, Mei & Su, Shiwei & Wang, Qiuwang & Yang, Jian, 2022. "Numerical study on temperature rise and mechanical properties of winding in oil-immersed transformer," Energy, Elsevier, vol. 239(PA).
    8. Arslan, Talha & Bulut, Y. Murat & Altın Yavuz, Arzu, 2014. "Comparative study of numerical methods for determining Weibull parameters for wind energy potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 820-825.
    9. Usman Zafar & Neil Kellard & Dmitri Vinogradov, 2022. "Multistage optimization filter for trend‐based short‐term forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 345-360, March.
    10. Álvaro Jaramillo-Duque & Nicolás Muñoz-Galeano & José R. Ortiz-Castrillón & Jesús M. López-Lezama & Ricardo Albarracín-Sánchez, 2018. "Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance," Energies, MDPI, vol. 11(2), pages 1-12, February.
    11. Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.
    12. Godina, Radu & Rodrigues, Eduardo M.G. & Matias, João C.O. & Catalão, João P.S., 2016. "Smart electric vehicle charging scheduler for overloading prevention of an industry client power distribution transformer," Applied Energy, Elsevier, vol. 178(C), pages 29-42.
    13. Verdejo, Humberto & Awerkin, Almendra & Becker, Cristhian & Olguin, Gabriel, 2017. "Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 512-521.
    14. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    15. El-Kharashi, Eyhab, 2014. "Detailed comparative study regarding different formulae of predicting the iron losses in a machine excited by non-sinusoidal supply," Energy, Elsevier, vol. 73(C), pages 513-522.
    16. Jianxiong Gao & Yuanyuan Liu & Yiping Yuan & Fei Heng, 2023. "Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    17. Stefan Wolny & Adam Krotowski, 2020. "Analysis of Polarization and Depolarization Currents of Samples of NOMEX ® 910 Cellulose–Aramid Insulation Impregnated with Mineral Oil," Energies, MDPI, vol. 13(22), pages 1-18, November.
    18. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.
    19. Liu Yuan & Jianzhong Zhou, 2017. "Self-Optimization System Dynamics Simulation of Real-Time Short Term Cascade Hydropower System Considering Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2127-2140, May.
    20. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.

    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:jeners:v:14:y:2021:i:5:p:1498-:d:513451. 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.