Existing high-voltage alternating current (AC) transmission line fault ranging methods have several drawbacks, including weak transition resistance, a complicated feature extraction process, and difficult calibration of the travelling wave head. To address these issues, a single-end fault ranging method for high-voltage AC transmission lines based on Attention-GRU and modulus amplitude ratio is proposed. Firstly, based on the travelling wave dispersion characteristics, an approximate formula is derived between the fault distance of the high-voltage AC transmission line and the amplitude ratio of the sum of the initial transient voltage travelling wave modes 1 and 2 and the mode 0 components at the ranging location. This shows that a definite nonlinear mapping relationship exists between the two. Secondly, the Attention-GRU is constructed using the multiscale wavelet modal maxima ratio between the sum of the initial transient voltage travelling wave mode 1 and 2 components and the mode 0 component as the input eigenquantities and the fault distance as the output quantity. The fault distance is then calculated using the Attention-GRU and the modal amplitude ratio. The Attention-GRU neural network fault ranging model is then constructed using the distance as the output quantity. After training is completed, the fault feature quantities obtained from the measurement points are inputted into the Attention-GRU model to achieve the purpose of fault ranging. The ranging ability of this model is then compared with that of other neural network models. A large number of simulations verify that the proposed method has high ranging accuracy and that the ranging capability is not affected by the fault type, transition resistance or the initial phase angle of the fault.
Yin et al. (Mon,) studied this question.