Surender Kumar Yellagoud, Purnachandra Rao Talluri, and Gondlala N. Sreenivas
Fault location, power distribution networks, fault classiﬁcation, adaptive network-based fuzzy inference system
One of the most important functions of distribution automation is automated fault location. The knowledge-based techniques are becoming more successful in enhancing the accuracy of fault location in power distribution networks. Adaptive network-based fuzzy inference system (ANFIS) with hybrid learning algorithm was employed in this article to arrive at an accurate fault location. Three
main estimations were done by the ANFIS fault models designed and developed in MATLAB r – fault type classiﬁcation, faulted line-segment detection, and fault location on that faulted line segment. This estimated information is vital for distribution substation engineer, and greatly facilitates the substation maintenance crew in
reaching the faulty spot quickly for repair and power restoration. The results obtained demonstrate higher levels of accuracy and eﬃciency, and thereby can greatly enhance the power system reliability and quality.