A Metadata-based Data Fabric Method for Intelligent Sensing Scenarios in Converter Stations

Digital converter stations are pivotal nodes in the construction of new power systems, with the IoT sensing network centered on integrated terminals being key infrastructure in the IoT sensing system of digital converter stations. When performing state intelligent sensing and decision-making, intelligent sensing scenarios in converter stations require extensive data access and integration. However, due to data privacy, heterogeneity among different nodes in the power grid, and transmission and storage costs, practical challenges arise, such as the challenges of diverse metadata sources, intricate integration processes, and underutilization across heterogeneous nodes. To address these issues, this paper designs a metadata-based data fabric architecture. Following the process of the identification, analysis, and integration of metadata, alongside constructing knowledge graphs, the overall utilization of different data sources is achieved. By analyzing the relationships between data sources, consolidating metadata via robust business interrelations, a comprehensive business knowledge graph for the entire intelligent converter station is constructed.