配电网电力设备缺陷文本智能辨识运维综述

doi:10.12204/j.issn.1000-7229.2022.05.010

电力建设 ›› 2022, Vol. 43 ›› Issue (5): 90-99.doi: 10.12204/j.issn.1000-7229.2022.05.010

配电网电力设备缺陷文本智能辨识运维综述

张磐¹, 郑悦², 李海龙³, 刘航旭⁴, 李国栋¹, 葛磊蛟⁴()

1.国网天津市电力公司电力科学研究院, 天津市 300384
2.国网天津市电力公司,天津市 300010
3.国网天津市电力公司滨海供电分公司,天津市 300450
4.天津大学电气自动化与信息工程学院,天津市 300072

收稿日期:2021-11-02 出版日期:2022-05-01 发布日期:2022-04-29
通讯作者: 葛磊蛟 E-mail:legendglj99@tju.edu.cn
作者简介:张磐(1983),男,硕士,高级工程师,主要研究方向为智能配电网和配电物联网技术。
郑悦(1982),男,硕士,高级工程师,主要研究方向为智能配电网技术。
李海龙(1988),男,硕士,高级工程师,主要研究方向为智能配电网技术。
刘航旭(1999),男,硕士研究生,主要研究方向为智能配电网技术。
李国栋(1978),男,硕士,高级工程师,主要研究方向为电能质量分析。
基金资助:
国网天津市电力公司科技项目(KJ20-1-07)

Overview on Intelligent Text Identification and Maintenance of Power Equipment Defects in Distribution Network

ZHANG Pan¹, ZHENG Yue², LI Hailong³, LIU Hangxu⁴, LI Guodong¹, GE Leijiao⁴()

1. State Grid Tianjin Electric Power Company Electrical Power Research Institute, Tianjin 300384, China
2. State Grid Tianjin Electric Power Company, Tianjin 300010, China
3. Binhai Electrical Branch, State Grid Tianjin Electric Power Company, Tianjin 300450, China
4. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Received:2021-11-02 Online:2022-05-01 Published:2022-04-29
Contact: GE Leijiao E-mail:legendglj99@tju.edu.cn
Supported by:
Science and Technology Projects of State Grid Tianjin Electric Power Company(KJ20-1-07)

摘要/Abstract

摘要：

面向配电网日常运行积累的大量电力设备缺陷文本,深入挖掘缺陷文本的特征,获知电力设备态势,是智能配电网精细化发展的重要方向。然而我国在该领域研究成果较少,缺少对此领域中重大挑战和针对性解决技术的分析。为此,文章对我国电力设备缺陷文本挖掘的主要技术进行了全面评价,并分析了面临的困难。以电力设备缺陷文本为研究对象,首先从错误识别与质量提升介绍电力设备缺陷文本挖掘的关键技术,进而分别从缺陷严重等级自动分类、缺陷细节提取、健康状态自动评价等方面阐述了其他智能配电网电力设备缺陷文本挖掘技术。进一步,结合未来智能配电网的发展趋势,对电力设备缺陷文本挖掘的前景进行了展望,以期为智能配电网精益化运维提供借鉴。

关键词: 电力大数据, 电力设备, 缺陷文本, 数据挖掘

Abstract:

Aiming at the large number of power equipment defect texts accumulated in the daily operation of distribution network, deeply mining the characteristics of defect texts and learning the power equipment situation is an important direction for the refinement development of intelligent distribution network. However, there are few research results in this field in China, and there is lack of analysis of key challenges and targeted solutions. Therefore, this paper comprehensively evaluates the main technologies of text mining of power equipment defects in China, and analyzes the difficulties faced. Taking the power equipment defect text as the research object, this paper firstly introduces the key technologies of text mining of power equipment defects from error identification and quality improvement, and then expounds other text mining technologies for power equipment defects in intelligent distribution network from the aspects of automatic classification of defect severity level, defect detail extraction and automatic health state evaluation. Furthermore, combined with the development trend of intelligent distribution network in the future, the future of text mining technologies for power equipment defects is prospected, in order to provide reference for lean operation and maintenance of intelligent distribution network.

Key words: power massive data, power equipment, defect text, data mining

中图分类号:

张磐, 郑悦, 李海龙, 刘航旭, 李国栋, 葛磊蛟. 配电网电力设备缺陷文本智能辨识运维综述[J]. 电力建设, 2022, 43(5): 90-99.

ZHANG Pan, ZHENG Yue, LI Hailong, LIU Hangxu, LI Guodong, GE Leijiao. Overview on Intelligent Text Identification and Maintenance of Power Equipment Defects in Distribution Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(5): 90-99.

图/表 5

图1 电力设备缺陷文本深度挖掘技术示意图

Fig.1 Schematic diagram of text deep mining technology for power equipment defects

图2 缺陷文本缺陷严重等级自动分类方法

Fig.2 Automatic classification method for defect severity level in defect text

表1 现有缺陷文本缺陷严重等级自动分类方法特点对比

Table 1 Comparison of characteristics of existing automatic classification methods for defect severity level of defect text

图3 构建语义框架流程

Fig.3 Semantic for framework construction

图4 电力设备文本依存句法树生成流程

Fig.4 Flowchart of constructing dependency syntax tree based on equipment defect text

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配电网电力设备缺陷文本智能辨识运维综述

Overview on Intelligent Text Identification and Maintenance of Power Equipment Defects in Distribution Network

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