An Intelligent Method for Fault Location in AC Cables Using Extreme Learning Machine

In high voltage cables, due to the mutual induction between the core and the sheath as well as the high capacitance of the cable, the fault location in alternative current (AC) cable is more complicated than the head transmission line. By using distance protection scheme for AC transmission line, the seen impedance by the relay has a nonlinear behavior with respect to fault location. In this paper, with the help of extreme learning machine (ELM), the fault locating algorithm is implemented by using the measured values of voltage and current of core and sheath on both sides of the cable. The proposed algorithm can detect the non-linear and complicated relations between measured quantities and fault location. In the system under study, at first, the core to sheath faults are simulated in the PSCAD/EMTDC software considering different fault resistances and different fault distances. Then, in order to train the intelligent core of the proposed method, input vectors are extracted for different conditions and a desirable output is considered corresponding to the fault distance. Examination of the results obtained from the use of various intelligent tools shows the superiority of the ELM over the ANN and SVM in terms of accuracy of and learning speed.