High-Accuracy Fault Location Detection in Double-Circuit Transmission Lines Utilizing Extreme Learning Machine Algorithm

Traditional fault-location methods applied to a double-circuit transmission line are usually implemented according to an abc-domain or a sequence network equivalent circuit that also varies with fault type. However, this dependence of those fault location algorithms from the line parameters suffers from inaccuracy injected under varying environmental conditions altering the parameters of the double-circuit transmission line. Herein, an Extreme Learning Machine-based line parameter-independent fault location method is suggested that can learn the nonlinear relationship between the voltages, currents measured, and fault locations with high accuracy. In the presented paper, the proposed method is simulated for different fault types at random distances in a power grid containing a double-circuit transmission line. The simulated data are then utilized for training the intelligent fault location system. Further, different distances and resistances fault locations are estimated to check the accuracy of the proposed approach. The obtained results are compared with the results of two other intelligent fault detection approaches, such as ANN and SVM and better accuracy and reliability are shown in ELM. The outputs of these tests show considerable improvements in the proposed technique of fault location on a double circuit transmission line under different environmental conditions.