Research on fault data enhancement method of smart grid based on generation-countermine neural network

Yulu Cao; Na Qu; Honglei Zhou; Shuhui Wu; Qingqing Wang

doi:10.1117/12.3004850

16 August 2023 Research on fault data enhancement method of smart grid based on generation-countermine neural network

Yulu Cao, Na Qu, Honglei Zhou, Shuhui Wu, Qingqing Wang

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870N (2023) https://doi.org/10.1117/12.3004850
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

In the modern power system, the safe and stable operation of the power transmission network and the special communication network depends on the healthy connection of its supporting network. In order to study and realize the above functions, a large amount of accurate data is very necessary. In this paper, the generation network G (Generator) and the discriminator network D (Discriminator) are used to continuously approach the real partial discharge voltage signal samples. Based on the WGAN model, the auxiliary classifier Wasserstein with gradient penalty is added, and the ACWGAN-GP model is proposed. Through the comparison between the data samples generated by the model and the real data samples, it can be found that there is a relatively consistent fluctuation rule between the two, but the values of the two remain different at the same time, which increases the diversity of the synthetic partial discharge voltage signal samples, is conducive to enhancing the robustness of the partial discharge detection model, and improves its generalization ability to detect faults. This provides a reference for enhancing the fault data of smart grids.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yulu Cao, Na Qu, Honglei Zhou, Shuhui Wu, and Qingqing Wang "Research on fault data enhancement method of smart grid based on generation-countermine neural network", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870N (16 August 2023); https://doi.org/10.1117/12.3004850

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KEYWORDS

Data modeling

Education and training

Neural networks

Statistical modeling

Gallium nitride

Network architectures

Signal generators

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