Big Data Architecture Design of Smart Grid Based On Big Data Platform

• Published in: Procedia computer science Vol. 228; pp. 356 - 365
• Main Authors: Yang, Liu, Wang, Tao, Sun, Bowen, Guo, Jiangtao, Li, Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2023
• Subjects: Big Data Architecture; Intuitive Visualization; Random Matrix Theory; Smart Grid; Big Data Architecture; Intuitive Visualization; Smart Grid; Random Matrix Theory
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2023.11.041

A novel big data-driven approach has been proposed to address the challenges of extensive data and user application difficulties in the smart grid. A smart grid big data architecture based on a big data platform has been designed. The random matrix theory is applied to establish a random matrix algorithm model, which samples data from the smart grid big database and undergoes training and learning to construct a data model for user requirements. Through calculations, the macroscopic data hidden in the smart grid big data is transformed into microscopic data for users' reference, enabling the identification of parameters that affect the normal operation of the smart grid at its core. The computed data can be displayed locally and also uploaded remotely to the SG186 marketing system for various user applications. Additionally, the data can be permanently stored in the cloud through wireless communication. By incorporating the random matrix theory algorithm into the smart grid big data architecture, the proposed solution not only enhances the intuitive visualization of smart grid big data but also provides technical references for future work.