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.