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

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhèng-Hóng Lin, Jiaxin Lin
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引用次数: 1

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.
基于深度学习的新型电力系统知识管理研究
随着信息技术的飞速发展,电力系统得到了迅速的发展和应用。在电力系统中,故障检测非常重要,是保证电力系统正常运行的关键手段之一。如何有效地提高故障检测能力是电力系统研究的重要课题。传统的故障检测主要依靠人工日常巡检,维护时必须断电,影响电网的正常运行。在紧急情况下,设备不能关闭电源,可能会导致错过测试,埋下安全隐患。为了解决这些问题,本文采用深度学习技术对基于知识管理的电力系统进行了研究。具体来说,我们首先介绍了基于知识管理的电力系统中的数据扩充和相关的激活功能。然后,我们开发了深度网络架构来提取基于知识管理的电力系统数据中的局部空间特征。在基于知识管理的电力系统中,我们进一步提出了几种基于交叉熵损失函数的数据分类训练策略。最后,通过实验验证了所提方法在基于知识管理的电力系统中的有效性。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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