A Grid Status Analysis Method with Large-Scale Wind Power Access Using Big Data

Dan Liu, Yiqun Kang, Heng Luo, Xiaotong Ji, Kan Cao and Hengrui Ma ()
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Dan Liu: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Yiqun Kang: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Heng Luo: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Xiaotong Ji: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Kan Cao: State Grid Hubei Electric Power Research Institute, Wuhan 430077, China
Hengrui Ma: School of Electrical and Automation, Wuhan University, Wuhan 430077, China

Energies, 2023, vol. 16, issue 12, 1-12

Abstract: Targeting the problem of the power grid facing greater risks with the connection of large-scale wind power, a method for power grid state analysis using big data is proposed. First, based on the big data, the wind power matrix and the branch power matrix are each constructed. Second, for the wind energy matrix, the eigenvalue index in the complex domain and the spectral density index in the real domain are constructed based on the circular law and the M-P law, respectively, to describe the variation of wind energy. Then, based on the concept of entropy and the M-P law, the index for describing the variation of the branch power is constructed. Finally, in order to analyze the real-time status of the grid connected to large-scale wind power, the proposed index is combined with the sliding time window. The simulation results based on the enhanced IEEE-33 bus system show that the proposed method can perform real-time analysis on the grid state of large-scale wind power connection from different perspectives, and its sensitivity is good.

Keywords: large-scale wind power access; power grid status analysis; wind power matrix; branch power matrix; random matrix; entropy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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