DocumentCode :
2099438
Title :
Power Quality Disturbances Recognition Based on PCA and BP Neural Network
Author :
Huang, Nantian ; Lin, Lin
Author_Institution :
Coll. of Inf. & Control Eng., Jilin Inst. of Chem. Technol., Jilin, China
fYear :
2010
fDate :
28-31 March 2010
Firstpage :
1
Lastpage :
4
Abstract :
This paper presents a new approach for recognizing disturbances signals in power quality (PQ) disturbances by principal-component analysis (PCA) and BP neural network. The new approach identifies most types of PQ disturbance, such as voltage sags, swells, interruptions, transients, harmonics and flickers. The new model mainly includes three steps. Firstly, S-transform is used to analyze power system disturbance signals, and 18 distinguishing features are extracted from the result of S-transform. Secondly, principal-component analysis (PCA) is used to reduce the dimensionality of features data set, mean while extract principal-components to describe nonstationary signals of power system. Finally, use the principal-components as the input vectors of BP neural network modified by adaptive learning factor, and classify the disturbances signals. The simulation results show the validity and efficiency of the proposed model.
Keywords :
backpropagation; fault diagnosis; neural nets; power engineering computing; power supply quality; principal component analysis; BP neural network; PCA; S-transform; adaptive learning factor; disturbances signal classification; power quality disturbances; power quality disturbances recognition; principal-component analysis; voltage interruptions; voltage sags; voltage swells; Data mining; Neural networks; Power quality; Power system analysis computing; Power system harmonics; Power system modeling; Power system simulation; Power system transients; Principal component analysis; Signal analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
Type :
conf
DOI :
10.1109/APPEEC.2010.5448666
Filename :
5448666
Link To Document :
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