Enhancement of Power Equipment Management Using Knowledge Graph
April 28, 2019 Β· Declared Dead Β· π 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)
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Authors
Yachen Tang, Tingting Liu, Guangyi Liu, Jie Li, Renchang Dai, Chen Yuan
arXiv ID
1904.12242
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.DB
Citations
66
Venue
2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)
Last Checked
3 months ago
Abstract
Accurate retrieval of the power equipment information plays an important role in guiding the full-lifecycle management of power system assets. Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process. Knowledge graph has been widely developed in large part owing to its schema-less nature. It enables the knowledge graph to grow seamlessly and allows new relations addition and entities insertion when needed. This study proposes an approach for constructing power equipment knowledge graph by merging existing multi-source heterogeneous power equipment related data. A graph-search method to illustrate exhaustive results to the desired information based on the constructed knowledge graph is proposed. A case of a 500 kV station example is then demonstrated to show relevant search results and to explain that the knowledge graph can improve the efficiency of power equipment management.
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