Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line
Abstract Due to the difficulty and time‐consumption in locating short‐distance transmission lines for deep‐sea offshore wind farm (DOWF)，this paper proposes a novel double‐terminal fault location method by using Stockwell‐transform (ST) and random‐forest (RF). After the fault type and branch are acc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-08-01
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| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12170 |
| _version_ | 1798035771096039424 |
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| author | Xiaodong Wang Xing Gao Yingming Liu Yonghao Wang |
| author_facet | Xiaodong Wang Xing Gao Yingming Liu Yonghao Wang |
| author_sort | Xiaodong Wang |
| collection | DOAJ |
| description | Abstract Due to the difficulty and time‐consumption in locating short‐distance transmission lines for deep‐sea offshore wind farm (DOWF)，this paper proposes a novel double‐terminal fault location method by using Stockwell‐transform (ST) and random‐forest (RF). After the fault type and branch are accurately determined, the accurate transmission line fault location is located. Firstly, Stockwell‐transform is employed to extract fault eigenvalues from the collected wind turbine (WT) current signals, which will reduce the sensitivity of eigenvalues to noise. And the Pearson correlation coefficient (PCC) is introduced to remove duplicate eigenvalues. Secondly, the reserved fault eigenvalues are taken as inputs to the different random‐forest to classify fault types and identify the fault branch, respectively. Finally, the double‐terminal fault location principle is established in fault negative sequence network (only ABCG uses positive sequence components). Newton‐Raphson method (NRM) is used to eliminate the influence of asynchrony data, which implies an accurate transmission line fault location for deep‐sea offshore wind farm. More than 4000 fault cases data obtained by Simulink simulation verify the feasibility and performance of the proposed method. The results show that the proposed location method has a high fault recognition rate and is immune to fault inception angle, resistance, location and noise. |
| first_indexed | 2024-04-11T21:01:44Z |
| format | Article |
| id | doaj.art-4d855e66ca0f4687a036cf78391d5f48 |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-11T21:01:44Z |
| publishDate | 2021-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-4d855e66ca0f4687a036cf78391d5f482022-12-22T04:03:27ZengWileyIET Renewable Power Generation1752-14161752-14242021-08-0115112368238210.1049/rpg2.12170Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission lineXiaodong Wang0Xing Gao1Yingming Liu2Yonghao Wang3Institute of Electrical Engineering Shenyang University of Technology Shenyang 110870 ChinaInstitute of Electrical Engineering Shenyang University of Technology Shenyang 110870 ChinaInstitute of Electrical Engineering Shenyang University of Technology Shenyang 110870 ChinaInstitute of Electrical Engineering Shenyang University of Technology Shenyang 110870 ChinaAbstract Due to the difficulty and time‐consumption in locating short‐distance transmission lines for deep‐sea offshore wind farm (DOWF)，this paper proposes a novel double‐terminal fault location method by using Stockwell‐transform (ST) and random‐forest (RF). After the fault type and branch are accurately determined, the accurate transmission line fault location is located. Firstly, Stockwell‐transform is employed to extract fault eigenvalues from the collected wind turbine (WT) current signals, which will reduce the sensitivity of eigenvalues to noise. And the Pearson correlation coefficient (PCC) is introduced to remove duplicate eigenvalues. Secondly, the reserved fault eigenvalues are taken as inputs to the different random‐forest to classify fault types and identify the fault branch, respectively. Finally, the double‐terminal fault location principle is established in fault negative sequence network (only ABCG uses positive sequence components). Newton‐Raphson method (NRM) is used to eliminate the influence of asynchrony data, which implies an accurate transmission line fault location for deep‐sea offshore wind farm. More than 4000 fault cases data obtained by Simulink simulation verify the feasibility and performance of the proposed method. The results show that the proposed location method has a high fault recognition rate and is immune to fault inception angle, resistance, location and noise.https://doi.org/10.1049/rpg2.12170Wind power plantsPower system protectionPower engineering computing |
| spellingShingle | Xiaodong Wang Xing Gao Yingming Liu Yonghao Wang Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line IET Renewable Power Generation Wind power plants Power system protection Power engineering computing |
| title | Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line |
| title_full | Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line |
| title_fullStr | Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line |
| title_full_unstemmed | Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line |
| title_short | Stockwell‐transform and random‐forest based double‐terminal fault diagnosis method for offshore wind farm transmission line |
| title_sort | stockwell transform and random forest based double terminal fault diagnosis method for offshore wind farm transmission line |
| topic | Wind power plants Power system protection Power engineering computing |
| url | https://doi.org/10.1049/rpg2.12170 |
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