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

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  • 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
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    References listed on IDEAS

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    Cited by:

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    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.

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