Fault Classification in Transmission Lines Using Random Forest and Notch Filter

Abstract

Overhead energy transmission lines are highly susceptible to failure. To deal with this problem, some researchers have proposed different preprocessing stages, which comprise mainly feature extraction, selection, and dimension reduction for fault classification in transmission lines. The common techniques applied in the preprocessing stage are the wavelet and Fourier transforms. For the classification stage, the most used method is artificial neural network. This work aims to show the use of random forest method with a simple preprocessing step based on notch filter to classify faults in transmission lines. The performance of the model was compared with that obtained by a neural network to show its efficiency. Using k-fold cross-validation to train, test, and compare the models, it was obtained the mean accuracy of 89.59% for the neural network and 91.96% for the random forest for testing data. In the validation process, it was obtained accuracy of 96.49% and 91.49% for neural network and random forest models, respectively. Although the neural network model has shown better generalization capacity, the random forest model performed about eight times faster than the neural network.