DocumentCode :
2597470
Title :
Load characteristics clustering of dynamic modeling data
Author :
Li, Peiqiang ; Li, Xinran ; Yuan, Xiaofang
Author_Institution :
Dept. of Electr. Eng., Hunan Univ., Changsha, China
fYear :
2009
fDate :
6-7 April 2009
Firstpage :
1
Lastpage :
4
Abstract :
Load current data can be regarded as stochastic disturbance of voltage. Three decompositions and re-construction of wavelet package method are be used to analyze load modeling data, and characterizes vector of load data are accurately constructed which is used to classify load data. The characteristics vector was standardized, then load data was classified by fuzzy subtract clustering in the paper. The proposed method has been proved validity by the examples of the attaining characteristics and clustering of dynamic lab and transformer substation data, which is high precision and convergence. It has the significant to load modeling processing the amount of load data.
Keywords :
fuzzy set theory; pattern clustering; power systems; wavelet transforms; dynamic lab clustering; dynamic modeling data; fuzzy subtract clustering; load characteristics clustering; load current data; load modeling processing; power system simulation; transformer substation data; voltage stochastic disturbance; wavelet package method; Data mining; Load modeling; Packaging; Power system analysis computing; Power system measurements; Power system modeling; Power system simulation; Stochastic processes; Substations; Wavelet packets; Load characteristics; decomposition and re-construction; subtract clustering; wavelet package;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4934-7
Type :
conf
DOI :
10.1109/SUPERGEN.2009.5347925
Filename :
5347925
Link To Document :
بازگشت