WAMS/SCADA Data Fusion Method Study Based on Time-Series Data Correlation Mining

  • LiJin Zhao
  • Liang Huang
  • Qiansu Lv
  • Tao Yang
  • Daqian Wei
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 658)

Abstract

Hybrid measurement state estimation of WAMS data and the SCADA system is an effective method to improve the traditional state estimation. However, as the WAMS data and the SCADA data belong to different systems, there are great differences between them. To solve this problem, WAMS/SCADA data fusion method based on the correlation mining of time-series data is proposed in this paper. Firstly, WAMS/SCADA correlation estimation is done with the derivation of Pearson correlation coefficient. Then, solving the function model for the time difference issue and the alignment problem of correlation curves. After that, analyzing the measurement precision by considering the measurement weight and calculate the matrix of time series data weight to complete the optimization for the measurement precision. Finally, forming the effective fusion scheme based on the correlation of timing data. Simulation results on the IEEE 118 nodes system, with set a comparison of different hybrid measurement state estimation and different state estimation algorithm, effectiveness and stability of the proposed method has been proved.

Keywords

Time-series data correlation mining WAMS/SCADA data fusion 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • LiJin Zhao
    • 1
  • Liang Huang
    • 1
  • Qiansu Lv
    • 1
  • Tao Yang
    • 1
  • Daqian Wei
    • 2
  1. 1.Electric Power Research Institute of Guizhou Power Grid Co., LtdGuiyangChina
  2. 2.School of Electrical EngineeringWuhan UniversityWuhanChina

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