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This paper presents an innovative approach in line commutated converter-based high-voltage direct-current (LCC-HVDC) transmission line for recognizing the section of the faults. In this work, three adaptive neuro-fuzzy inference system (ANFIS) modules have been suggested to investigate the safety responsibilities in the LCC-HVDC line. The ANFIS-1 aims to ascertain the faults in both sections; ANFIS-2 is to distinguish the section of the fault; and ANFIS-3 is to recognize the characteristics of the fault. To ascertain the effectiveness of the ANFIS approach, it has been compared with the differential protection (DP), wavelet transform (WT), fuzzy and artificial neural network (ANN). Furthermore, the proposed methods have been tested by varying the fault resistance (up to 500 Ω), power angle (from 5 to 15 degrees), near and end faults, and changing the transmission line parameters (±15 %). The simulation outcomes are disclosed that the proposed ANFIS technique yields better performance than other studied methods in terms of percentage of accuracy, time of fault section recognition, and reach setting.

Faults identification in high voltage direct current transmission lines using adaptive neuro-fuzzy inference system