Paper Info
Title
A New Strategy Based on Feature Selection for Fault Classification in Transmission Lines.
Abstract
The transmission lines are the element most susceptible to faults on power systems, and the short circuit faults are the worst type of faults than can happen on this element. In order to avoid further problems due to these faults, a fault diagnostic is necessary, and the use of front ends is required. However, the selection process for choosing the front ends is not a simple one because it behaves differently for each. Therefore, this paper presents a new front end, called Concat front end, which integrates other front ends, such as wavelet, raw and Root Mean Square. Furthermore, we have applied feature selection techniques based on filter in order to decrease the dimension of the input data. Thus, we used the following classifiers: neural network, K-nearest neighbor, Random Forest and support vector machine. We used a public dataset called UFPAFaults to train and test the classifiers. As a result, the concatenation of front ends, on most cases, had achieved the lowest error rates. In addition, the feature selection techniques applied showed that it is possible to get higher accuracy using less features on the process.
Year
DOI
Venue
2016
10.1007/978-3-319-47955-2_31
ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2016
Keywords
Field
DocType
Short-circuit fault,Transmission lines,Front ends,Feature selection,Machine learning algorithms
Front and back ends,Pattern recognition,Feature selection,Computer science,Support vector machine,Electric power transmission,Concatenation,Artificial intelligence,Artificial neural network,Random forest,Wavelet
Conference
Volume
ISSN
Citations 
10022
0302-9743
0
PageRank 
References 
Authors
0.34
7
8
Name
Order
Citations
PageRank
Márcia Homci100.34
Paulo Chagas200.34
Brunelli Miranda300.34
Jean Freire400.34
Raimundo Viegas Jr.500.34
Yomara Pires641.48
Bianchi Serique Meiguins75628.03
Jefferson Morais844.18