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

Author:

Lin Zhengping,Lin Jiaxin

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.

Publisher

European Alliance for Innovation n.o.

Subject

Information Systems and Management,Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Software

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Intelligent Edge Caching and Computing for Scalable Information Systems;ICST Transactions on Scalable Information Systems;2023-06-22

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