Fault Classification in Transmission Lines with Generalization Competence

Leandro Augusto Ensina, Luiz Eduardo Soares de Oliveira, Eduardo Cunha de Almeida, Signie Laureano França Santos, Leandro Silva Bernardino

Abstract: Transmission lines are crucial components of the electric power system and are exposed to several conditions that can disrupt the transmission of electrical power. In this scenario, a protection system must detect and classify a fault, for example, to enable the quick repair and restoration of a faulty line. This paper presents a method for fault type classification with two main characteristics: (1) independence of the sampling rate of the protection system; and (2) capacity to classify failures for different transmission lines than the one used to train the algorithm (generalization competence). Initially, the first post-fault cycle of the three-phase current signal is used to get the groundDetection feature, which aims to indicate the action or not of the ground in the failure. Next, maximum and minimum values are obtained from a pre-fault cycle of the current waveform to normalize the post-fault cycle by the MinMax technique, separately for each phase and individually for each example. Lastly, we get energy-based attributes together with maximum and minimum values from these normalized cycles for each phase to create the feature vector along with the groundDetection attribute. The extracted features are then used as input to the Random Forest algorithm to predict the fault type. The results demonstrate average accuracy higher than 99% for diversified simulated events for both characteristics previously mentioned. Our method also manifested the capacity to classify real fault events even being trained with synthetic examples with an accuracy of 96%.