Online Fault Detection for Power System using Wavelet and PNN

被引：0

作者：

Othman, Mohd Fauzi ^{[1
]}

Amari, Hudabiyah Arshad ^{[1
]}

机构：

[1] Univ Teknol Malaysia, Fac Elect Engn, Skudai, Malaysia

来源：

2008 IEEE 2ND INTERNATIONAL POWER AND ENERGY CONFERENCE: PECON, VOLS 1-3 | 2008年

关键词：

Fault detection; Multi-resolution analysis; Probabilistic neural network; Power system; Wavelet transform;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents the approach to the problem of fast fault detection in transmission line. The idea is to use concepts from signal processing and wavelet theory to create fast and sensitive fault detection. Then, the artificial neural network was used to classify the fault location in the transmission line. In this study, the output signal of the speed deviations of generator are taken as the input for wavelet analysis. The "oscillation signatures" are recorded using Multi Resolution Analysis (MRA) Wavelet Transform. The MRA decomposes the signal where the components are analyzed for their energy content and characteristic and then used as a feature for different classes and locations of the fault. The same features are also fed to the Probabilistic Neural Network (PNN) to give the location and classification of the fault.

引用

页码：1638 / 1642

页数：5

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