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Image fusion of fault detection in power system based on deep learning

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Abstract

Aiming at the three main problems of power system—leakage, high temperature and physical damage, a new image fusion of fault detection method in power system based on deep learning is proposed in this paper. The core of deep learning is achieved by capsule network model. The model is trained and tested by self-built image dataset of power system. There are three types of dataset: visible images,infrared images and ultraviolet images. After being preprocessed and feature-extracted, the visible image is used as the fusion image background, the infrared image provides the thermal information of power equipment, and the ultraviolet image provides the electric field information on the exterior of power equipment. The collected images are decomposed into corresponding high frequency component image and low frequency component image respectively, which reconstructed into fused images by the capsule network model. With the registration of the three types of images, the faults in the power system can be detected and displayed accurately in the fused image.

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Acknowledgements

This paper is acknowledged by the National Natural Science Foundation of China (Grant: 51502209), the Government Support Enterprise Development Funding of Hubei Province (Grant: 16441), the Three-dimensional Textiles Engineering Research Center of Hubei Province, the Anqing Technology Transfer Center of Wuhan Textile University.

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Authors and Affiliations

  1. Wuhan Textile University, Wuhan, China

    Yu Li, Fengyuan Yu, Qian Cai, Kun Yuan, Renzhuo Wan, Meiyu Qian, Pengfeng Liu, Junwen Guo, Juan Yu, Tian Zheng, Huan Yan, Peng Hou, Yiming Feng, Siyuan Wang & Lei Ding

  2. China University of Geosciences, Wuhan, China

    Xiaoying Li

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  1. Yu Li
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  15. Siyuan Wang
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  16. Lei Ding
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Corresponding author

Correspondence to Qian Cai.

Additional information

Yu Li, Fengyuan Yu, Qian Cai, Kun Yuan, and Renzhuo Wan are co-first authors.

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Li, Y., Yu, F., Cai, Q. et al. Image fusion of fault detection in power system based on deep learning. Cluster Comput 22 (Suppl 4), 9435–9443 (2019). https://doi.org/10.1007/s10586-018-2264-2

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  • DOI: https://doi.org/10.1007/s10586-018-2264-2

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