Intelligent Fault Diagnosis of Substation Equipment on theBasis of Deep Learning and Knowledge Graph

doi:10.12204/j.issn.1000-7229.2022.03.008

Abstract

Abstract:

Finding and diagnosing problems in the operation of substation equipment in time is one of the key means to ensure the safe operation of power grids. On the basis of the research of deep network and knowledge graph, this paper proposes an intelligent fault diagnosis method that correlates multi-modal information of substation equipment. Deep learning technology and knowledge graph method are used to extract and fuse knowledge from the collected multi-modal data, and a semantic knowledge graph of multi-modal information fusion is constructed. YOLOv4 algorithm is used to cluster fault samples and extract prior bounding box parameters. The multi-modal knowledge map and YOLOv4 visual inspection are combined together and applied to the substation scene to realize the autonomous early warning and diagnosis of the substation equipment. Experiments show that the model can achieve the goal of intelligent decision-making of fault diagnosis, thereby improving the efficiency of daily operation, maintenance and management of power grid.

Key words: multi-modal, intelligent diagnosis, deep learning, knowledge graph

CLC Number:

TM63

XIAO Falong, WU Yuezhong, SHEN Xuehao, HE Zhenkai, QIN Ye. Intelligent Fault Diagnosis of Substation Equipment on theBasis of Deep Learning and Knowledge Graph[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 66-74.

Figures/Tables 11

Fig.1 Technical framework of the overall research

Fig.2 Construction process of knowledge graph

Fig.3 Network structure of YOLOv4

Table 1 Configuration of experimental environment

Table 2 Table 2 network parameters

Fig.4 Schematic diagram of image annotation

Table 3 Comparison of detectionperformance of the algorithms

Fig.5 Partial detection results of YOLOv4network on the test images

Fig.6 Graph of substation fault knowledge (part)

Fig.7 Knowledge graph query

Table 4 Diagnostic analysis records

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