Blind source separation method for multi-source ultra-high frequency partial discharge signals

Lijuan Xie; Jiachuan Long

doi:10.1117/12.2672148

21 March 2023 Blind source separation method for multi-source ultra-high frequency partial discharge signals

Lijuan Xie, Jiachuan Long

Proceedings Volume 12609, International Conference on Computer Application and Information Security (ICCAIS 2022); 126090Q (2023) https://doi.org/10.1117/12.2672148
Event: International Conference on Computer Application and Information Security (ICCAIS 2022), 2022, ONLINE, ONLINE

Abstract

Ultra-high frequency (UHF) partial discharge (PD) monitoring is an important technique to detect and evaluate the internal insulation state of high-voltage equipment. In practice, multiple insulation defects may exist. As a result, the measured UHF PD signals are probably the mixture from multiple PD sources, making the current single-source-based fault diagnosis methods inapplicable. In this paper, a blind source separation algorithm based on sparse component analysis (SCA) is proposed for the separation of overlapped UHF PD signals. First, the raw signals are mapped to a two-dimensional time-frequency (TF) spectrum by using the short-time Fourier transform (STFT). Second, the peak detection technology and fuzzy clustering technology are adopted to estimate the number of PD sources and the mixing matrix. Finally, the l1-norm decomposition algorithm is applied to reconstruct the TF representations of the source signals, and the recovered source signals in time-domain are obtained by the inverse STFT (ISTFT). Numerical and experimental studies are conducted to validate the effectiveness of the proposed method.

Citation Download Citation

Lijuan Xie and Jiachuan Long "Blind source separation method for multi-source ultra-high frequency partial discharge signals", Proc. SPIE 12609, International Conference on Computer Application and Information Security (ICCAIS 2022), 126090Q (21 March 2023); https://doi.org/10.1117/12.2672148

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