Fault Locating in HVDC Transmission Lines Using Generalized Regression Neural Network and Random Forest Algorithm

This paper presents a novel method based on machine learning strategies for fault locating in high voltage direct current (HVDC) transmission lines. In the proposed fault-location method، only post-fault voltage signals measured at one terminal are used for feature extraction. In this paper، due to high dimension of input feature vectors، two different estimators including the generalized regression neural network (GRNN) and the random forest (RF) algorithm are examined to find the relation between the features and the fault location. The results of evaluation using training and test patterns obtained by simulating various fault types in a long overhead transmission line with different fault locations، fault resistance and pre-fault current values have indicated the efficiency and the acceptable accuracy of the proposed approach.