期刊
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
卷 33, 期 3, 页码 402-410出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2010.10.001
关键词
Power system transient; Wavelet analysis; Entropy weight; Artificial neural network; WEE; WEW
资金
- National Natural Science Foundation of China [50877068]
- Program for New Century Excellent Talents in University [NCET-06-0799]
- Chinese National Science Fund [50407009]
- Sichuan Province Distinguished Scholars Fund [006ZQ026-012]
- Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control, Ministry of Education, PR China [KW02002]
The detection and classification of transient signals are widely applied in many fields of power system. The study of transient signal detection and classification is a sustaining focus of researchers as well as a difficult issue. There are still many problems needed to be solved in this area. Based on the wavelet transform (WT), the idea of entropy and weight coefficient is introduced, and the wavelet energy entropy (WEE) and wavelet entropy weight (WEW) are defined in this paper. The distribution picture of WEE and WEW along with scales are presented for the first time. PSCAD/EMTDC models for six types of transients, namely breaker switching, capacitor switching, short circuit fault, primary arc, lightning disturbance and lightning strike fault, are constructed. With WEE and WEW, the eigenvectors for the six transients are established and a model which uses the eigenvectors as the input of the BP (back-propagation) neural network is set up to realize the classification of these transients. The simulation has been executed based on a 500 kV transmission line model in China and the results show that feature extraction based on WEE and WEW can effectively discover the useful local features. With the help of neural network classifier, it has effective classifying result. This method is applicable in the power system. (C) 2010 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据