Data Management Framework and Operation Optimization Method for Regional Wind Farms Big Data Center Based on Cloud-edge-end Collaboration
The big data center(BDC)of regional wind farms is faced with challenges such as proliferation of low opera-tional energy efficiency,poor interactivity,and duplication of resources,which makes it difficult to adapt to the demands of data processing for real-time,green computing in new power systems.This paper systematically sorted out the typical digital services and their sensitivity to computing power in wind farms.The spatial and temporal adjustable potentials of BDC loads are also analyzed.Then,a collaborative framework is constructed for data management,which decomposes wind farm big data management into three levels,including cloud,edge and end.The corresponding key technologies on data interaction,business execution,resource scheduling and quality governance are proposed.The experiments are per-formed to analyze the performance of the proposed architecture in terms of data transfer,storage and energy consumption.The results show that the proposed cloud-edge-end collaborative architecture can save 350%of storage capacity and re-duce 21.04%of server energy consumption for cloud servers in big data centers compared to traditional centralized architectures,which further verify the effectiveness and rationality of the proposed architecture.
regional wind farmsbig data centerdigitalization businesscloud-edge-end collaborationdata management frameworkoperation optimization
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