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- Title
Wavelet entropy analysis and machine learning classification model of DC serial arc fault in electric vehicle power system.
- Authors
Kun Xia; Bangzheng Liu; Xiale Fu; Haotian Guo; Sheng He; Wei Yu; Jingjun Xu; Hui Dong
- Abstract
Electric vehicle (EV) power system is flammable and explosive when the direct current (DC) arc occurs at elevated temperature. Thus, DC serial arc real-time monitoring is an insurance to keep away from disaster. In this study, the detection algorithm of DC serial arc is proposed. The wavelet entropy algorithm, the classification model based on support vector machine and logistic regression are analysed separately. The above algorithms are combined to identify the DC serial arc faults effectively under different types of loads in EV power system. The results show that the combined algorithm has a good performance of DC serial arc detection with high accuracy and robustness compared with a simple approach. Meanwhile, the false detection rate of the detection algorithm is close to zero, which could ensure the safety and stable operation of the system.
- Subjects
ELECTRIC power system faults; ELECTRIC arc; WAVELETS (Mathematics); MACHINE learning; SUPPORT vector machines
- Publication
IET Power Electronics (Wiley-Blackwell), 2019, Vol 12, Issue 15, p1
- ISSN
1755-4535
- Publication type
Article
- DOI
10.1049/iet-pel.2019.0375