Compression of big data collected in wind farm based on tensor train decomposition

Abstract

To address the storage challenges stemming from large volumes of heterogeneous data in wind farms, we propose a data compression technique based on tensor train decomposition (TTD). Initially, we establish a tensor-based processing model to standardize the heterogeneous data originating from wind farms, which includes both structured SCADA (supervisory control and data acquisition) data and unstructured video and picture data. Subsequently, we introduce a TTD-based method designed to compress the heterogeneous data generated in wind farms while preserving the inherent spatial eigenstructure of the data. Finally, we validate the efficacy of the proposed method in alleviating data storage challenges by utilizing authentic wind farm datasets. Comparative analysis reveals that the TTD-based method outperforms previously proposed compression techniques, specifically the canonical polyadic (CP) and Tucker methods.