The key of traveling wave (TW) fault location (FL) method is the calculation of TW velocity and the calibration of TW wave head. In order to achieve high-precision FL, detailed transmission line (TL) parameters are needed to accurately calculate the TW velocity. In view of the problem that the existing high-precision TWFL methods are highly dependent on the calculation of TW velocity, the paper contributes to following aspects: (i) the mapping relationship between wavefront frequency and fault distance/fault pole is constructed; (ii) a neural network model suitable for FL and fault pole identification is designed, which is optimized by Particle Swarm Optimization (PSO) algorithm and Levenberg–Marquardt (L-M) algorithm; (iii) a complete FL scheme is proposed. In order to verify the FL accuracy of proposed method, an ±800 kV bipolar high-voltage direct-current (HVDC) transmission system model is constructed using PSCAD/EMTDC. According to the simulation results, the errors of FL is less than 0.2 km and the accuracy of fault pole identification is 100%. For the problems of fault type, fault resistance and noise, the proposed method shows high robustness.
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