DocumentCode :
572343
Title :
An Artificial Neural Network for Pollution Evaluation Based on Leakage Current
Author :
Zhou Jianbo ; Zhang Qiaogen ; Xi Haibo ; Gao Bo
Author_Institution :
Sch. of Electr. Eng., Xi´an Jiaotong Univ., Xi´an, China
fYear :
2012
fDate :
27-29 March 2012
Firstpage :
1
Lastpage :
4
Abstract :
An artificial neural network for pollution evaluation is introduced in this paper which contributes to design an expert system for flashover predicting. The network measures four parameters (peak impulse current, maximum low-frequency current, humidity and voltage) which reflect the insulating property of an insulator and outputs the pollution level. After enough of training, the outputs of the network show great agreement with the experiment results. However, accuracy of the network is influenced by the training sample.
Keywords :
air pollution control; expert systems; flashover; insulation; insulators; leakage currents; learning (artificial intelligence); neural nets; power engineering computing; artificial neural network; expert system design; flashover prediction; humidity parameter; insulator insulating property; leakage current; maximum low-frequency current parameter; peak impulse current parameter; pollution evaluation; training sample; voltage parameter; Artificial neural networks; Insulators; Leakage current; Neurons; Pollution; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific
Conference_Location :
Shanghai
ISSN :
2157-4839
Print_ISBN :
978-1-4577-0545-8
Type :
conf
DOI :
10.1109/APPEEC.2012.6307639
Filename :
6307639
Link To Document :
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