@Article{ee.2026.076354,
AUTHOR = {Guanghua He, Jinlong Qi, Yao Feng, Jiayi Han, Heng Chen, Baoming Huang, Jiangtao Li},
TITLE = {Fault Location of Distribution Network Based on Traveling Wave Head Inversion},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/25590},
ISSN = {1546-0118},
ABSTRACT = {The identification of the traveling wave head is an important factor affecting the accuracy of fault traveling wave positioning. In practice, in addition to the attenuation of traveling wave amplitude and rising speed caused by distribution line factors, various traveling wave sensors can also cause transmission distortion of high-frequency traveling wave signals, which in turn affects the calibration of traveling wave arrival time and the accuracy of fault distance measurement. The inversion technology of sensor transmission characteristics using analytical methods has limited ability to reflect factors such as stray capacitance and sensor differences. In comparison, data-driven artificial intelligence modeling inversion technology can better capture the details of sensor transmission characteristics and help improve positioning accuracy. Regarding this issue, a fault location method for distribution lines based on initial traveling wave head inversion is proposed in this paper. Firstly, the mechanism of fault traveling wave propagation distortion, including distribution lines and sensor factors, was analyzed. Furthermore, a traveling wave head inversion method based on a one-dimensional convolutional neural network was proposed. This method constructs an inversion model by training the detail coefficients of the primary and secondary traveling wave heads of the sensor after the wavelet transform as samples. Based on the training model, the wavelet detail coefficients of the secondary side wave after the fault occurs are inverted into the corresponding primary side coefficients, and the arrival time of the wave head is accurately extracted through the wavelet modulus maximum method, thereby improving the positioning accuracy of the double-ended traveling wave method. Experiments show that the average location error of this method does not exceed 2.2% under various fault conditions with a sampling rate of 1 MHz, and demonstrating the robustness and reliability of this method in complex fault scenarios.},
DOI = {10.32604/ee.2026.076354}
}