IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i22p4378-d287991.html
   My bibliography  Save this article

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

Author

Listed:
  • Haobo Xu

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
    Fujian Smart Electrical Engineering Technology Research Center, Fuzhou 350108, China)

  • Zhenguo Shao

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
    Fujian Smart Electrical Engineering Technology Research Center, Fuzhou 350108, China)

  • Feixiong Chen

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
    Fujian Smart Electrical Engineering Technology Research Center, Fuzhou 350108, China)

Abstract

The electric arc furnace (EAF) contributes to almost one-third of the global iron and steel industry, and its harmonic pollution has drawn attention. An accurate EAF harmonic model is essential to evaluate the harmonic pollution of EAF. In this paper, a data-driven compartmental modeling method (DCMM) is proposed for the multi-mode EAF harmonic model. The proposed DCMM considers the coupling relationship among different frequencies of harmonics to enhance the modeling accuracy, meanwhile, the dimensions of the harmonic dataset are reduced to improve computational efficiency. Furthermore, the proposed DCMM is applicable to establish a multi-mode EAF harmonic model by dividing the multi-mode EAF harmonic dataset into several clusters corresponding to the different modes of the EAF smelting process. The performance evaluation results show that the proposed DCMM is adaptive in terms of establishing the multi-mode model, even if the data volumes, number of clusters, and sample distribution change significantly. Finally, a case study of EAF harmonic data is conducted to establish a multi-mode EAF harmonic model, showing that the proposed DCMM is effective and accurate in EAF modeling.

Suggested Citation

  • Haobo Xu & Zhenguo Shao & Feixiong Chen, 2019. "Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace," Energies, MDPI, vol. 12(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4378-:d:287991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/22/4378/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/22/4378/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kirschen, Marcus & Badr, Karim & Pfeifer, Herbert, 2011. "Influence of direct reduced iron on the energy balance of the electric arc furnace in steel industry," Energy, Elsevier, vol. 36(10), pages 6146-6155.
    2. Raul Garcia-Segura & Javier Vázquez Castillo & Fernando Martell-Chavez & Omar Longoria-Gandara & Jaime Ortegón Aguilar, 2017. "Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient," Energies, MDPI, vol. 10(9), pages 1-11, September.
    3. Gajic, Dragoljub & Savic-Gajic, Ivana & Savic, Ivan & Georgieva, Olga & Di Gennaro, Stefano, 2016. "Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks," Energy, Elsevier, vol. 108(C), pages 132-139.
    4. Steven Lecompte & Oyeniyi A. Oyewunmi & Christos N. Markides & Marija Lazova & Alihan Kaya & Martijn Van den Broek & Michel De Paepe, 2017. "Case Study of an Organic Rankine Cycle (ORC) for Waste Heat Recovery from an Electric Arc Furnace (EAF)," Energies, MDPI, vol. 10(5), pages 1-16, May.
    5. Bhonsle, Deepak C. & Kelkar, Ramesh B., 2016. "Analyzing power quality issues in electric arc furnace by modeling," Energy, Elsevier, vol. 115(P1), pages 830-839.
    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. Joon-Ho Kim & Jin-O Kim, 2020. "Analysis and Mitigation on Switching Transients of Medium-Voltage Low-Harmonic Filter Banks," Energies, MDPI, vol. 13(9), pages 1-14, May.
    2. Rafael S. Salles & Sarah K. Rönnberg, 2023. "Review of Waveform Distortion Interactions Assessment in Railway Power Systems," Energies, MDPI, vol. 16(14), pages 1-33, July.

    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. Manojlović, Vaso & Kamberović, Željko & Korać, Marija & Dotlić, Milan, 2022. "Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters," Applied Energy, Elsevier, vol. 307(C).
    2. Zbigniew Łukasik & Zbigniew Olczykowski, 2020. "Estimating the Impact of Arc Furnaces on the Quality of Power in Supply Systems," Energies, MDPI, vol. 13(6), pages 1-30, March.
    3. Raul Garcia-Segura & Javier Vázquez Castillo & Fernando Martell-Chavez & Omar Longoria-Gandara & Jaime Ortegón Aguilar, 2017. "Electric Arc Furnace Modeling with Artificial Neural Networks and Arc Length with Variable Voltage Gradient," Energies, MDPI, vol. 10(9), pages 1-11, September.
    4. Zbigniew Olczykowski, 2022. "Arc Voltage Distortion as a Source of Higher Harmonics Generated by Electric Arc Furnaces," Energies, MDPI, vol. 15(10), pages 1-23, May.
    5. Miha Kovačič & Klemen Stopar & Robert Vertnik & Božidar Šarler, 2019. "Comprehensive Electric Arc Furnace Electric Energy Consumption Modeling: A Pilot Study," Energies, MDPI, vol. 12(11), pages 1-13, June.
    6. Pili, Roberto & Romagnoli, Alessandro & Jiménez-Arreola, Manuel & Spliethoff, Hartmut & Wieland, Christoph, 2019. "Simulation of Organic Rankine Cycle – Quasi-steady state vs dynamic approach for optimal economic performance," Energy, Elsevier, vol. 167(C), pages 619-640.
    7. Jie Yang & Shaowen Lu & Liangyong Wang, 2020. "Fused magnesia manufacturing process: a survey," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 327-350, February.
    8. Samet, Haidar & Ghanbari, Teymoor & Ghaisari, Jafar, 2014. "Maximizing the transferred power to electric arc furnace for having maximum production," Energy, Elsevier, vol. 72(C), pages 752-759.
    9. Chen, Zhengjie & Ma, Wenhui & Wu, Jijun & Wei, Kuixian & Yang, Xi & Lv, Guoqiang & Xie, Keqiang & Yu, Jie, 2016. "Influence of carbothermic reduction on submerged arc furnace energy efficiency during silicon production," Energy, Elsevier, vol. 116(P1), pages 687-693.
    10. Haendel, Michael & Hirzel, Simon & Süß, Marlene, 2022. "Economic optima for buffers in direct reduction steelmaking under increasing shares of renewable hydrogen," Renewable Energy, Elsevier, vol. 190(C), pages 1100-1111.
    11. Haikarainen, Carl & Shao, Lei & Pettersson, Frank & Saxén, Henrik, 2024. "Mathematical optimization modeling for scenario analysis of integrated steelworks transitioning towards hydrogen-based reduction," Energy, Elsevier, vol. 305(C).
    12. van Kleef, Luuk M.T. & Oyewunmi, Oyeniyi A. & Markides, Christos N., 2019. "Multi-objective thermo-economic optimization of organic Rankine cycle (ORC) power systems in waste-heat recovery applications using computer-aided molecular design techniques," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    13. Li, Xiaoya & Xu, Bin & Tian, Hua & Shu, Gequn, 2021. "Towards a novel holistic design of organic Rankine cycle (ORC) systems operating under heat source fluctuations and intermittency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    14. Michael Chukwuemeka Ekwonu & Mirae Kim & Binqi Chen & Muhammad Tauseef Nasir & Kyung Chun Kim, 2023. "Dynamic Simulation of Partial Load Operation of an Organic Rankine Cycle with Two Parallel Expanders," Energies, MDPI, vol. 16(1), pages 1-18, January.
    15. Ramos, Alba & Chatzopoulou, Maria Anna & Freeman, James & Markides, Christos N., 2018. "Optimisation of a high-efficiency solar-driven organic Rankine cycle for applications in the built environment," Applied Energy, Elsevier, vol. 228(C), pages 755-765.
    16. Bożena Gajdzik & Radosław Wolniak & Wies Grebski, 2023. "Process of Transformation to Net Zero Steelmaking: Decarbonisation Scenarios Based on the Analysis of the Polish Steel Industry," Energies, MDPI, vol. 16(8), pages 1-36, April.
    17. Zbigniew Olczykowski & Zbigniew Łukasik, 2021. "Evaluation of Flicker of Light Generated by Arc Furnaces," Energies, MDPI, vol. 14(13), pages 1-23, June.
    18. Qin, Shiyue & Chang, Shiyan, 2017. "Modeling, thermodynamic and techno-economic analysis of coke production process with waste heat recovery," Energy, Elsevier, vol. 141(C), pages 435-450.
    19. Qiu, Ziyang & Yue, Qiang & Yan, Tianyi & Wang, Qi & Sun, Jingchao & Yuan, Yuxing & Che, Zichang & Wang, Yisong & Du, Tao, 2023. "Gas utilization optimization and exergy analysis of hydrogen metallurgical shaft furnace," Energy, Elsevier, vol. 263(PC).
    20. Chatzopoulou, Maria Anna & Lecompte, Steven & Paepe, Michel De & Markides, Christos N., 2019. "Off-design optimisation of organic Rankine cycle (ORC) engines with different heat exchangers and volumetric expanders in waste heat recovery applications," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    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:12:y:2019:i:22:p:4378-:d:287991. 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.