High resistance grounding fault identification of distribution network based on improved residual network

Yan Wu; Shilei Guan; Xiaoli Meng; Yan Wu

doi:10.1117/12.2646826

7 September 2022 High resistance grounding fault identification of distribution network based on improved residual network

Yan Wu, Shilei Guan, Xiaoli Meng, Yan Wu

Author Affiliations +

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123292D (2022) https://doi.org/10.1117/12.2646826
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

It is conducive to fault treatment and protection to effectively identify the type of single-phase high resistance grounding fault in distribution network. A single-phase grounding fault type identification method, based on improved residual network, is proposed. Firstly, the direct features of waveform data are automatically obtained by using the feature extraction module in the residual network.The time-frequency characteristics of the original fault electrical quantity are analyzed by Fourier transform, and the energy domain characteristics are added on this basis.Secondly, the manually extracted features are replaced by the original waveform data, and the single-phase grounding fault classification model of distribution network based on improved residual network is established. Finally, through the verification of field recording data, compare the original fault data with that after extracting multi-domain features. It is proved that this method can quickly and effectively complete the high resistance grounding fault identification, and has no requirements for the actual waveform length and sampling rate, so it is more practical.

Citation Download Citation

Yan Wu, Shilei Guan, Xiaoli Meng, and Yan Wu "High resistance grounding fault identification of distribution network based on improved residual network", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123292D (7 September 2022); https://doi.org/10.1117/12.2646826

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