To address the complex noise interference caused by the increasing complexity of distribution network topologies and harsh operating environments, as well as the resulting impacts and challenges to the accuracy of traveling wave fault location, the authors introduce a method for traveling wave front detection, which combines an improved wavelet threshold function denoising, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), and the teager energy operator (TEO). Firstly, to suppress the impact of noise interference on wavefront detection, this paper constructs an improved wavelet threshold function to denoise the fault signals, thereby enhancing the signal-to-noise ratio (SNR) and providing high-fidelity input for subsequent wavefront calibration. Secondly, the ICEEMDAN method is applied for decompose the denoised signals, generating multiple intrinsic mode functions (IMFs) that represent the characteristics of the signal. Finally, the TEO is applied to the high-frequency component IMF1, which best represents the properties of the traveling wave front. The moment corresponding to the peak of its energy spectrum is identified as the arrival time of the initial traveling wave front. Experimental results indicate that at a sampling rate of 10 MHz, the proposed method performs excellently under noisy conditions and complex structures, achieving an average positioning error of 21.54 m, with a maximum error not exceeding 51.80 m. The method remains stable under various fault conditions and sampling rates. Even in a strong noise environment with a SNR as low as 10 dB, it can reliably identify the wavefront, demonstrating outstanding anti-noise performance and robustness.
Liu et al. (Thu,) studied this question.