DocumentCode :
437375
Title :
A novel discretization scheme combined entropy with rough set theory for transient stability assessment based on ANN
Author :
Yan, Liu ; Xueping, Gu ; Jun, Li
Author_Institution :
Sch. of Electr. Eng., North China Electr. Power Univ., Baoding, China
Volume :
1
fYear :
2004
fDate :
21-24 Nov. 2004
Firstpage :
119
Abstract :
Transient stability assessment (TSA) is of great importance to power system operation and control. In recent years, a large amount of research work has been devoted to the TSA approach based on artificial neural networks (TSAANN). The TSAANN has shown much promise. Noticeably, existing research has focused mainly on the ANN construction and training algorithm design. However, the data pretreatment, as an inevitable and important step of TSAANN, has not attracted much attention for a long time. In this paper, the data discretization, the kernel of the data pretreatment of TSAANN is studied. A new discretization scheme combined entropy with rough sets theory (RST) for TSAANN is proposed. Simulation results indicate the validity of the proposed scheme for TSAANN.
Keywords :
entropy; neural nets; power engineering computing; power system transient stability; rough set theory; ANN; artificial neural networks; data discretization; data pretreatment; discretization scheme; entropy; power system control; power system operation; rough set theory; training algorithm; transient stability assessment; Algorithm design and analysis; Artificial neural networks; Control systems; Entropy; Kernel; Power system control; Power system stability; Power system transients; Power systems; Set theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power System Technology, 2004. PowerCon 2004. 2004 International Conference on
Print_ISBN :
0-7803-8610-8
Type :
conf
DOI :
10.1109/ICPST.2004.1459977
Filename :
1459977
Link To Document :
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