| Autor: |
Shuo Jin, Yiyang E, Lin Zhu, Chu Li, Yuchen Yang, Ziwei Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
AIP Advances, Vol 14, Iss 6, Pp 065015-065015-10 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2158-3226 |
| DOI: |
10.1063/5.0208498 |
| Popis: |
Inter-turn short circuit fault (ISCF) is the main fault type of dry-type air-core reactors (DARs), and the reactor burnout event caused by winding ISCF seriously threatens the safe and stable operation of the electric power system. In order to effectively curb the further worsening of ISCF in DARs, a fault detection method based on frequency response analysis was proposed. First, the effect of ISCF on the frequency response curve of the windings was investigated based on the distributed parameter model of DARs. Then, four sets of eigenvalues were proposed for ISCF diagnosis based on the influence law and curve data analysis theory. Finally, based on the eigenvalues, the support vector machine model optimized by the sparrow search algorithm was used to effectively classify the degree of ISCF of the reactor, and the accuracy reached 96%. Furthermore, a test platform was built based on the theoretical analysis for experimental verification, and the test results are consistent with the simulation results. Hence, the method proposed in this paper could effectively classify the ISCF and its severity in DARs. The research in this paper could provide effective guidance for the diagnosis of ISCF in DARs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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