دورية أكاديمية
PRPD data analysis with Auto-Encoder Network
| العنوان: | PRPD data analysis with Auto-Encoder Network |
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| المؤلفون: | Li Songyuan, Man Yuyan, Zhang Chi, Fang Qiong, Li Suya, Deng Min |
| المصدر: | E3S Web of Conferences, Vol 81, p 01019 (2019) |
| بيانات النشر: | EDP Sciences, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Environmental sciences |
| مصطلحات موضوعية: | Environmental sciences, GE1-350 |
| الوصف: | Gas Insulated Switchgear (GIS) is related to the stable operation of power equipment. The traditional partial discharge pattern recognition method relies on expert experience to carry out feature engineering design artificial features, which has strong subjectivity and large blindness. To address the problem, we introduce an encoding-decoding network to reconstruct the input data and then treat the encoded network output as a partial discharge signal feature. The adaptive feature mining ability of the Auto-Encoder Network is effectively utilized, and the traditional classifier is connected to realize the effective combination of the deep learning method and the traditional machine learning method. The results show that the features extracted based on this method have better recognition than artificial features, which can effectively improve the recognition accuracy of partial discharge. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English French |
| تدمد: | 2267-1242 |
| Relation: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/07/e3sconf_wrem2018_01019.pdf; https://doaj.org/toc/2267-1242 |
| DOI: | 10.1051/e3sconf/20198101019 |
| URL الوصول: | https://doaj.org/article/cffcdcc962e14a6db299e0009edf4552 |
| رقم الأكسشن: | edsdoj.ffcdcc962e14a6db299e0009edf4552 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22671242 |
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| DOI: | 10.1051/e3sconf/20198101019 |