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Fault identification based on BP neural network and wavelet packet in power systems
| Content Provider | Scilit |
|---|---|
| Author | Tao, Fang Qian, Sun Hangli, Jian Ning, Li Yan, Zhou Yanjie, She Zhangao, Li Ying, Cai Leiyu, Zhao Jiang, Li |
| Copyright Year | 2021 |
| Description | Journal: Iop Conference Series: Earth and Environmental Science This paper proposes a fault identification method based on BP neural network and wavelet packet, which extracts the fault transient eigenvalues of the three-phase current and zero-sequence current from measurement data under the fault conditions. Firstly, the eigenvalues of three-phasors are sampled under the typical faults, such as single-phase ground fault, two-phase short-circuit fault, two-phase ground short-circuits fault, and three-phase short-circuit fault. Secondly, the three-phase current and the zero-sequence current are subjected to wavelet packet transform to extract the eigenvalues, which are viewed as the input of the neural network to determine the fault type. Finally, simulation results show that the proposed method can give reliable identification results under different fault conditions. |
| Related Links | https://iopscience.iop.org/article/10.1088/1755-1315/645/1/012057/pdf |
| ISSN | 17551307 |
| e-ISSN | 17551315 |
| DOI | 10.1088/1755-1315/645/1/012057 |
| Journal | Iop Conference Series: Earth and Environmental Science |
| Issue Number | 1 |
| Volume Number | 645 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Earth and Environmental Science Hardware and Architecture |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Physics and Astronomy Environmental Science |
