Enhanced distance protection for HVDC lines using adaptive neuro-fuzzy inference systems.

Abstract

Accuracy and speed of fault detection are crucial to the performance of DC transmission systems. In this paper, a novel approach is proposed for fault detection, classification, and localization in high-voltage DC transmission lines (HVDC-TLs). The proposed approach for protecting HVDC-TLs by designing and operating a distance protection scheme has been constructed using a fuzzy inference system and training an adaptive neuro-fuzzy inference system. A fuzzy inference system model is proposed to detect faults, classify them, and determine the zone where the fault occurred. The transmission line is divided into three zones to facilitate fault location identification. An adaptive neuro-fuzzy inference system is then trained to determine the fault location per kilometer. The proposed distance protection scheme identifies faults with high fault resistance; it can be successfully used to estimate the fault area and locate faults in HVDC-TLs using the concept of fuzzy inference. A monopolar DC transmission line system was modeled and operated, and several faults were simulated using PSCAD and MATLAB software. As clarified from the various simulation experiments, the proposed approach has performed better than the existing techniques and recently published related works.