[1]张保平,曾军,刘景立,等.电网二次设备消缺处理中的自学习方法[J].应用科技,2020,47(6):42-46.[doi:10.11991/yykj.202007005]
　ZHANG Baoping,ZENG Jun,LIU Jingli,et al.Self-learning methods in eliminating and handling defects of secondary equipment in power grid[J].Applied science and technology,2020,47(6):42-46.[doi:10.11991/yykj.202007005]
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电网二次设备消缺处理中的自学习方法(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年6期
页码:
42-46
栏目:
智能科学与技术
出版日期:
2021-01-31

文章信息/Info

Title:
Self-learning methods in eliminating and handling defects of secondary equipment in power grid
作者:
张保平1;  曾军1;  刘景立1;  马宜军2;  曹磊1;  丛雷3;  杨剑3;  赵子根3;  黎强3
1. 国网河北省电力有限公司 保定供电分公司，河北 保定 071000;
2. 国网河北省电力有限公司 检修分公司，河北 石家庄 050070;
3. 长园深瑞继保自动化有限公司，广东 深圳 518057
Author(s):
ZHANG Baoping1;  ZENG Jun1;  LIU Jingli1;  MA Yijun2;  CAO Lei1;  CONG Lei3;  YANG Jian3;  ZHAO Zigen3;  LI Qiang3
1. Baoding Power Supply Company, State Grid Hebei Electric Power Co., Ltd, Baoding 071000, China;
2. Maintenance Branch, State Grid Hebei Electric Power Co., Ltd, Shijiazhuang 050070, China;
3. CYG SUNRI Co., Ltd., Shenzhen 518057, China
关键词:
电网检修; 缺陷处置; 差分模型; 层次分析; k最邻近算法; 自学习
Keywords:
power grid maintenance; defects handling; difference model; analytic hierarchy process; k-nearest neighbor algorithm; self-learning
分类号:
TM77
DOI:
10.11991/yykj.202007005
文献标志码:
A
摘要:
针对电力系统结构复杂、数据量大，层次分析法(AHP)在缺陷处理中不能为决策提供新方案和权重难以确定等问题，本文利用k最邻近(kNN)算法能够处理电网设备中多分类问题和预测新类别的优点，同时利用差分模型能够避免经过权重比较后，直接判断测试文本所属类别的优势，将差分模型和k最邻近算法应用到层次分析方法中，降低了时间复杂度。结合专家库进行自学习，推理出缺陷处理的方案。经过某省电力公司的检修辅助决策系统的运行测试，证明该方法具有实用价值，有效地解决了上述问题，提高了缺陷处理的准确性，为电网检修提供了保障。
Abstract:
Due to complex structure of the power system and the excessive amount of data, analytic hierarchy process (AHP) cannot provide new solutions for decision-making and it is difficult to determine weight in handling defects. This paper uses the k-nearest neighbor (kNN) algorithm to deal with the multi-classification problems in power grid equipment and predict the new category. At the same time, the difference model can avoid the problem of directly determining the category of the test text after the weight comparison. A method of combining the difference model and kNN into AHP is proposed, it can reduce time complexity, and the method inferred the scheme of defects handling through self-learning of the expert database. Through the operation test of the maintenance and decision support system of a provincial power company, it proves that the method has practical effect and has effectively solved the problems of the power system and the excessive amount of data. The proposed method improves the accuracy of handling defects and provides protection for power grid maintenance.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-07-08。
基金项目:国网河北省电力有限公司科技项目(kj2019-023)
作者简介:张保平，男，工程师;赵子根，男，工程师
通讯作者:赵子根，E-mail：zhaozg@sznari.com
更新日期/Last Update: 2021-02-05