Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Geng-Jie Yang"'
Publikováno v:
IEEE Access, Vol 7, Pp 16051-16065 (2019)
Traditional arc-suppression devices have a weak effect on the arc-extinguishing result of earth fault because of increased active and harmonic components of fault current. To solve the problem, in this paper, a flexible arc-suppression device based o
Externí odkaz:
https://doaj.org/article/5021e2e8d8f64d4dbafc4f0d70f274b4
Publikováno v:
IEEE Journal of Photovoltaics. 11:219-231
In recent years, many supervised learning algorithms have been successfully applied for photovoltaic (PV) fault diagnosis. In practice, it is not possible to effectively obtain labels of large samples, limiting the engineering application of these al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::145892e5dd97d468c3f9d2cc52902e43
https://doi.org/10.1109/jphotov.2020.3038335
https://doi.org/10.1109/jphotov.2020.3038335
Publikováno v:
IEEE Transactions on Power Electronics. 35:7086-7099
Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02d4012ad241acd147a341ec97f07b2e
https://doi.org/10.1109/tpel.2019.2956812
https://doi.org/10.1109/tpel.2019.2956812
Publikováno v:
IEEE Sensors Journal. 19:9352-9363
Machine learning algorithm based on hand-crafted features from the raw vibration signal has shown promising results in the hydroelectric generating unit (HGU) fault diagnosis in recent years. Such methodologies, nevertheless, can lead to important in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ba647b087a6582147e32c7ee05ca3f5
https://doi.org/10.1109/jsen.2019.2926095
https://doi.org/10.1109/jsen.2019.2926095
Publikováno v:
2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT).
Machine learning based on hand-crafted features from the original aerial insulator image has shown promising results in the insulator defect detection in recent years. Such methodologies, nevertheless, are not suitable for detecting insulator defect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96152c6cec2c219b44577dcf0237e405
https://doi.org/10.1109/iccsnt47585.2019.8962497
https://doi.org/10.1109/iccsnt47585.2019.8962497
Publikováno v:
Electronics (2079-9292); Aug2022, Vol. 11 Issue 15, p2405-2405, 19p
Publikováno v:
IEEE Transactions on Power Electronics; Jul2020, Vol. 35 Issue 7, p7086-7099, 14p
Publikováno v:
IEEE Sensors Journal; 10/15/2019, Vol. 19 Issue 20, p9352-9363, 12p
Publikováno v:
Journal of Computational Methods in Sciences & Engineering; 2019, Vol. 19 Issue 1, p179-195, 17p
Publikováno v:
Health & Medicine Week; 3/22/2024, p2607-2607, 1p