The Detection of Power Quality Disturbances Based on Generalized S-transform
被引:0
作者:
Liang, Chengbin
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机构:
Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
Natl Inst Metrol, Beijing 100029, Peoples R ChinaHunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
Liang, Chengbin
[1
,2
]
Teng, Zhaosheng
论文数: 0引用数: 0
h-index: 0
机构:
Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R ChinaHunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
Teng, Zhaosheng
[1
]
Li, Jianmin
论文数: 0引用数: 0
h-index: 0
机构:
Hunan Normal Univ, Coll Engn & Design, Changsha 410081, Peoples R ChinaHunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
Li, Jianmin
[3
]
Liu, Fangxing
论文数: 0引用数: 0
h-index: 0
机构:
Natl Inst Metrol, Beijing 100029, Peoples R China
Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R ChinaHunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
Liu, Fangxing
[2
,4
]
He, Qing
论文数: 0引用数: 0
h-index: 0
机构:
Natl Inst Metrol, Beijing 100029, Peoples R ChinaHunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
He, Qing
[2
]
机构:
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
[2] Natl Inst Metrol, Beijing 100029, Peoples R China
[3] Hunan Normal Univ, Coll Engn & Design, Changsha 410081, Peoples R China
[4] Harbin Inst Technol, Sch Instrumentat Sci & Engn, Harbin 150001, Peoples R China
来源:
2020 CONFERENCE ON PRECISION ELECTROMAGNETIC MEASUREMENTS (CPEM)
|
2020年
基金:
中国国家自然科学基金;
国家重点研发计划;
关键词:
Gaussian window;
harmonics;
power quality;
time-frequency measurement;
TIME-FREQUENCY;
LOCALIZATION;
SPECTRUM;
D O I:
10.1109/cpem49742.2020.9191829
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
A modified generalized S transform is designed to extract the parameters of power quality disturbances such as starting and ending time, amplitude, harmonic frequencies more accurately in this paper. The demands of power quality detection is better met by controlling the Gaussian window function. Finally, the simulation results show that the excellent time-frequency performance of this method.
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