DocumentCode :
1759697
Title :
Flashover Voltage Prediction of Composite Insulators Based on the Characteristics of Leakage Current
Author :
Shihua Zhao ; Xingliang Jiang ; Zhijing Zhang ; Jianlin Hu ; Lichun Shu
Author_Institution :
State Key Lab. of Power Transm. Equip. & Syst. Security & New Technol., Chongqing Univ., Chongqing, China
Volume :
28
Issue :
3
fYear :
2013
fDate :
41456
Firstpage :
1699
Lastpage :
1708
Abstract :
Effective prediction of flashover voltage (FOV) of insulators is an important approach to the prevention of pollution flashover accidents. In order to predict the FOV of insulators and prevent pollution flashover accidents, first, a large number of artificial pollution tests, which simulate the impact of contamination level and hydrophobicity classification (HC) on FOV and leakage current, have been investigated. Second, based on the experimental data, the relationship between the FOV and contamination level, HC, has been obtained; the four characteristics of leakage current, namely, the entropy of pulse amplitude (S), the maximum pulse amplitude (Ih), the energy ration (K) and the energy (E), have been extracted. They jointly reflect how severe the contamination level and the HC of composite insulators are from different perspectives. Third, the variation laws between the four characteristics and the contamination level, HC, have been obtained. Finally, the FOV prediction least squares-support vector machines (LS-SVM) model has been presented, in which the four characteristics are used as the inputs of model, and the FOV is used as the output of model. The prediction results are basically consistent with the test results. Therefore, the model is acceptable to predict the FOV of composite insulators and is of significance for the prevention of pollution flashover accidents.
Keywords :
composite insulators; flashover; hydrophobicity; insulator contamination; insulator testing; leakage currents; least squares approximations; power engineering computing; support vector machines; FOV; HC; LS-SVM model; artificial pollution tests; composite insulators; contamination level; energy ration; flashover voltage prediction; hydrophobicity classification; leakage current characteristics; least squares-support vector machine model; pollution flashover accident prevention; pulse amplitude entropy; Flashover; Insulators; Leakage currents; Pollution; Predictive models; Surface contamination; Composite insulator; contamination level; flashover voltage prediction; hydrophobicity classification; leakage current;
fLanguage :
English
Journal_Title :
Power Delivery, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8977
Type :
jour
DOI :
10.1109/TPWRD.2013.2257879
Filename :
6527362
Link To Document :
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