Abnormal recognition of power equipment based on the improved neural network

Nan Yao; Yuxi Zhao; Jianhua Qin; Guangrui Shan; Xi Wu; Lvfu Zhu

doi:10.1117/12.2652269

10 November 2022 Abnormal recognition of power equipment based on the improved neural network

Nan Yao, Yuxi Zhao, Jianhua Qin, Guangrui Shan, Xi Wu, Lvfu Zhu

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123313F (2022) https://doi.org/10.1117/12.2652269
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

In order to ensure the normal operation of power system and improve the recognition accuracy of the substation abnormality, an abnormal recognition method based on improved neural network for power equipment is proposed. Firstly, the abnormal image of the substation is collected and denoised. Then, the features of the abnormal image are extracted and dimensionless processed. Thirdly, the improved neural network is used to establish the image recognition model of the abnormal image. Finally, the simulation experiment of this method is carried out. The experimental results show that the proposed method can not only obtain better recognition results, but also meet the detection requirements of the abnormal image.

Citation Download Citation

Nan Yao, Yuxi Zhao, Jianhua Qin, Guangrui Shan, Xi Wu, and Lvfu Zhu "Abnormal recognition of power equipment based on the improved neural network", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123313F (10 November 2022); https://doi.org/10.1117/12.2652269

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KEYWORDS

Neural networks

Transformers

Image processing

Wavelet transforms

Feature extraction

Wavelets

Genetic algorithms

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