A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism
In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/8/1904 |
| _version_ | 1827718799980232704 |
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| author | Ruixuan Yang Fulin Zhou Kai Zhong |
| author_facet | Ruixuan Yang Fulin Zhou Kai Zhong |
| author_sort | Ruixuan Yang |
| collection | DOAJ |
| description | In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method. |
| first_indexed | 2024-03-10T20:30:08Z |
| format | Article |
| id | doaj.art-74239a569c234b5896da351349e9153f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:30:08Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-74239a569c234b5896da351349e9153f2023-11-19T21:30:49ZengMDPI AGEnergies1996-10732020-04-01138190410.3390/en13081904A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution MechanismRuixuan Yang0Fulin Zhou1Kai Zhong2School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaIn railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method.https://www.mdpi.com/1996-1073/13/8/1904harmonic impedancetraction networkharmonic impedance identificationlinear regression modeldata evolution mechanism |
| spellingShingle | Ruixuan Yang Fulin Zhou Kai Zhong A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism Energies harmonic impedance traction network harmonic impedance identification linear regression model data evolution mechanism |
| title | A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism |
| title_full | A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism |
| title_fullStr | A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism |
| title_full_unstemmed | A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism |
| title_short | A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism |
| title_sort | harmonic impedance identification method of traction network based on data evolution mechanism |
| topic | harmonic impedance traction network harmonic impedance identification linear regression model data evolution mechanism |
| url | https://www.mdpi.com/1996-1073/13/8/1904 |
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