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

doi:10.12204/j.issn.1000-7229.2022.05.010

Abstract

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

CLC Number:

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.

Figures/Tables 5

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

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

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

Fig.3 Semantic for framework construction

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

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