High impedance fault detection based on wavelet transform and statistical pattern recognition
High impedance fault detection based on wavelet transform and statistical pattern recognition
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DOI：
10.1109/tpwrd.2005.852367
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发表时间：
2005-10
影响因子：
4.4
通讯作者：
A. Sedighi;M. Haghifam;O. Malik;M. Ghassemian
中科院分区：
文献类型：
--
作者：
A. Sedighi;M. Haghifam;O. Malik;M. Ghassemian
提出了一种基于模式识别系统的高阻抗故障检测新方法。利用该方法可以将HIF与绝缘子泄漏电流(ILC)和电容器投切、负载切换(高/低电压)、接地故障、涌流和无负载线切换等暂态信号区分开来。用小波变换对信号进行分解和特征提取，用主成分分析进行特征选择，用贝叶斯分类器进行分类。通过实验获得了HIF和ILC数据，并用EMTP程序模拟得到了暂态数据。结果表明，该方法能有效地将HIF与其他事件区分开来。
A novel method for high impedance fault (HIF) detection based on pattern recognition systems is presented in this paper. Using this method, HIFs can be discriminated from insulator leakage current (ILC) and transients such as capacitor switching, load switching (high/low voltage), ground fault, inrush current and no load line switching. Wavelet transform is used for the decomposition of signals and feature extraction, feature selection is done by principal component analysis and Bayes classifier is used for classification. HIF and ILC data was acquired from experimental tests and the data for transients was obtained by simulation using EMTP program. Results show that the proposed procedure is efficient in identifying HIFs from other events.