Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace
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 (DCM...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/22/4378 |
| _version_ | 1811302237919510528 |
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| author | Haobo Xu Zhenguo Shao Feixiong Chen |
| author_facet | Haobo Xu Zhenguo Shao Feixiong Chen |
| author_sort | Haobo Xu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:25:37Z |
| format | Article |
| id | doaj.art-2a82a02cd6464271ab23ef3998969deb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:25:37Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2a82a02cd6464271ab23ef3998969deb2022-12-22T02:56:30ZengMDPI AGEnergies1996-10732019-11-011222437810.3390/en12224378en12224378Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc FurnaceHaobo Xu0Zhenguo Shao1Feixiong Chen2College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaThe 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.https://www.mdpi.com/1996-1073/12/22/4378harmonic pollutionelectric arc furnace (eaf)data-driven compartmental modelingharmonic power-flow calculation |
| spellingShingle | Haobo Xu Zhenguo Shao Feixiong Chen Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace Energies harmonic pollution electric arc furnace (eaf) data-driven compartmental modeling harmonic power-flow calculation |
| title | Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace |
| title_full | Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace |
| title_fullStr | Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace |
| title_full_unstemmed | Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace |
| title_short | Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace |
| title_sort | data driven compartmental modeling method for harmonic analysis a study of the electric arc furnace |
| topic | harmonic pollution electric arc furnace (eaf) data-driven compartmental modeling harmonic power-flow calculation |
| url | https://www.mdpi.com/1996-1073/12/22/4378 |
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