sis 22(2): e7

Research Article

Research on Knowledge Management of Novel Power System Based on Deep Learning

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  • @ARTICLE{10.4108/eetsis.v9i6.2634,
        author={Zhengping Lin and Jiaxin Lin},
        title={Research on Knowledge Management of Novel Power System Based on Deep Learning},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={10},
        keywords={Deep learning, knowledge management, power system},
        doi={10.4108/eetsis.v9i6.2634}
    }
    
  • Zhengping Lin
    Jiaxin Lin
    Year: 2022
    Research on Knowledge Management of Novel Power System Based on Deep Learning
    SIS
    EAI
    DOI: 10.4108/eetsis.v9i6.2634
Zhengping Lin1,*, Jiaxin Lin2
  • 1: Electric Power Research Institute of China Southern Power Grid Company, China
  • 2: Guangdong Power Grid Co.,Ltd, China
*Contact email: zhengping_lin@hotmail.com

Abstract

With the rapid development of information technology, power system has been developed and applied rapidly. In the power system, fault detection is very important and is one of the key means to ensure the operation of power system. How to effectively improve the ability of fault detection is the most important issue in the research of power system. Traditional fault detection mainly relies on manual daily inspection, and power must be cut off during maintenance, which affects the normal operation of the power grid. In case of emergency, the equipment can not be powered off, which may lead to missed test and bury potential safety hazards. To solve these issues, in this paper, we study the knowledge management based power system by employing the deep learning technique. Specifically, we firstly introduce the data augmentation in the knowledge management based power system and the associated activated functions. We then develop the deep network architecture to extract the local spatial features among the data of the knowledge management based power system. We further provide several training strategies for the data classification in the knowledge management based power system, where the cross entropy based loss function is used. Finally, some experimental results are demonstrated to show the effectiveness of the proposed studies for the knowledge management based power system.