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Voltage Disturbance Signals Identification Based on ILMD and Neural Network

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成果类型:
期刊论文
作者:
Fan, Shaosheng*;Wang, Xuhong;Yang, Siyang
通讯作者:
Fan, Shaosheng
作者机构:
[Wang, Xuhong; Yang, Siyang; Fan, Shaosheng] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, 960,2nd Sect, Changsha, Peoples R China.
通讯机构:
[Fan, Shaosheng] C
Changsha Univ Sci & Technol, Sch Elect & Informat Engn, 960,2nd Sect, Changsha, Peoples R China.
语种:
英文
关键词:
Disturbing signal;ILMD;endpoint extension;signal decomposition;BP neural network
期刊:
International Journal of Pattern Recognition and Artificial Intelligence
ISSN:
0218-0014
年:
2020
卷:
34
期:
7
页码:
2058007
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61473049]; Hunan Provincial Key Laboratory [2018TP1025]
机构署名:
本校为第一且通讯机构
院系归属:
电气与信息工程学院
摘要:
In order to identify the disturbance signal in power system and reduce the influence on system security, a voltage disturbance signal classifier based on improved local mean decomposition (ILMD) and BP neural network is proposed. ILMD is used to decompose the disturbance signal in three layers, and the product function (PF) component with amplitude-frequency information of voltage signal is obtained. The signal energy value constructed by PF component is used as the input of BP neural network to identify and classify the voltage disturbance signal. Experiments on four typical voltage disturban...

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