Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Xiangui Xiao"'
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 11, Iss 6, Pp 1902-1911 (2023)
In the compression of massive compound power quality disturbance (PQD) signals in active distribution networks, the compression ratio (CR) and reconstruction error (RE) act as a pair of contradictory indicators, and traditional compression algorithms
Externí odkaz:
https://doaj.org/article/9058b7cd1bf84f7ba5e6499660ebd818
Autor:
Xiangui Xiao, Kaicheng Li
Publikováno v:
IEEE Access, Vol 9, Pp 152250-152260 (2021)
The traditional power quality disturbances classification methods include three stages, i.e., feature extraction, feature selection, classifier training. These methods suffer from low accuracy and a limited improvement margin. Since deep learning can
Externí odkaz:
https://doaj.org/article/d8775a8e800140b0b425564cadf68c0b
Autor:
Kaicheng Li, Xiangui Xiao
Publikováno v:
IEEE Access, Vol 9, Pp 152250-152260 (2021)
The traditional power quality disturbances classification methods include three stages, i.e., feature extraction, feature selection, classifier training. These methods suffer from low accuracy and a limited improvement margin. Since deep learning can
Publikováno v:
Journal of Physics: Conference Series. 1746:012061
The power quality signals are always mixed with noise, which will influence the accurate measurement and characteristic extraction. Therefore, it’s important to denoise for the signal processing. This paper puts forward a novel denoising algorithm
Publikováno v:
IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006).