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)


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.


Time-series data correlation mining WAMS/SCADA data fusion 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Phadke A G, Synchronized phasor measurements in power systems [J]. IEEE Computer Applications in Power, 1993, 6(2): 10-15.Google Scholar
  2. 2.
    Phadke A G, Pickett B, Adamiak M, et al.Synchronized sampling and phasor measurements for relay and control[J]. IEEE Trans. on Power Delivery, 1994, 9(1):442-452.Google Scholar
  3. 3.
    Yu Qinjie, Wang Xiaoru, You Jiaxun, et al, Equality constraints two-step state estimation model based on phasor measurements [J]. Power System Technology, 2007, 31(10): 84-88(in Chinese).Google Scholar
  4. 4.
    Qin Xiaohui, Bi Tianshu, Yang Qixun, A new method for hybrid nonlinear state estimation with PMU[J]. Automation of Power Systems, 2007, 31(4): 28-32(in Chinese).Google Scholar
  5. 5.
    LIU Jinfeng, WANG Shuyang Survey on applications of wide area measurement system in power system analysis [J] High Voltage Engineering, 2007, 33(7); 182-185.Google Scholar
  6. 6.
    DUAN Jundong, SUN Yankai, YIN Xiugang Voltage stability’ online prediction using WAMS[J]. High Voltage Engineering, 2009, 35(7): 1748-1752.Google Scholar
  7. 7.
    Ding Junce, Cai Zexiang, Wang Keying, Mixed measurements stateestimation based on WAMS[J]. Proceedings of the CSEE, 2006, 26(2): 58-63(in Chinese)Google Scholar
  8. 8.
    Adelfio G, Chiodi M, D’Alessandro A, Luzio D, D’Anna G, Mangano G. Simultaneous seismic wave clustering and registration. Computers & Geosciences, 2012, 44:60-69.Google Scholar
  9. 9.
    Ye L, Keogh E. Time series shapelets: A new primitive for data mining. In: Proc. of the 15th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2009. 947-956.Google Scholar
  10. 10.
    Liu X, Yang MCK. Simultaneous curve registration and clustering for functional data. Computational Statistics & Data Analysis, 2009, 53(4):1361-1376.Google Scholar
  11. 11.
    Yu Erkeng, Power system state estimation[M]. Beijing: China Water Power Press, 1985: 62-64(in Chinese).Google Scholar
  12. 12.
    State Grid Corporation of China. Technical specification for WAMS[S]. Beijing: State Grid Corporation of China, 2006(in Chinese).Google Scholar
  13. 13.
    Power System Relaying Committee of the IEEE Power Engineering Society. IEEE standard for synchrophasors for power systems [S]. New York: The Institute of Electrical and Electronics Engineers, Inc, 2005).Google Scholar
  14. 14.
    LI Dalu, LI Rui, SUN Yuanzhang. Data compatibility analysis of WAMS/SCADA hybrid measurements state estimation[J] Procceedings of the CSEE, 2010, 30(16): 60-66.Google Scholar
  15. 15.
    YOU Jiaxun, HUANG Bin, GUO Chuangxin, etal State estimation using SCADA and PMU hybrid measurements [J] High Voltage Engineering, 2009, 35(7): 1765-1796.Google Scholar
  16. 16.
    WU Xing, LIU Tianqi, LI Xingyuan, LI CongshanOptimal Configuration of PMU Based on Data Compatibility of WAMS/SCADA and Improved FCM Clustering Algorithm [J] Power System Technology, 2014, (03): 756-761.Google Scholar
  17. 17.
    Zhengbing Hu, Yevgeniy V.Bodyanskiy, Oleksii K. Tyshchenko, Olena O. Boiko,”Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARXModel”,International Journal of Information Technology and Computer Science (IJITCS), Vol.8, No.10, pp. 1-10, 2016. DOI: 10.5815/ijitcs.2016.10.01
  18. 18.
    Er. Garima Jain, Bhawna Mallick,”A Study of Time Series Models ARIMA and ETS”,International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.4, pp.57-63, 2017.DOI: 10.5815/ijmecs.2017.04.07
  19. 19.
    Ayman E. Khedr, S.E.Salama, Nagwa Yaseen,”Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis”, International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.7, pp.22-30, 2017. DOI:  10.5815/ijisa.2017.07.03
  20. 20.
    LIU Daowei1, SONG Dunwen1, WANG Hongyin2Voltage Stability Online Evaluation System Based on WAMS and EMS Power System Technology2014, (07): 1934-1938.Google Scholar
  21. 21.
    ZHOU Hong, LI Qiang, LIN Tao Power system disturbance and operation identification based on WAMS Electric Power Automation Equipment2011, (02): 7-11.Google Scholar
  22. 22.
    Li Hong, research on some issues of power system state estimation based on WAMS. [D] North China Electric Power University (Beijing), 2010.Google Scholar
  23. 23.
    JI Luyu, WU Junyong, ZHOU Yanzhen Transient Stability Prediction of Power System Based on WAMS Characteristic of Perturbed Voltage Trajectory Clusters [J]High Voltage Engineering 2015, (03): 807-814.Google Scholar
  24. 24.
    WANG Huaibao, SHEN Hong, ZHANG Ying, Grid Synchronization Method Based on Improved Adaptive Filter. [J]High Voltage Engineering 2014, (11): 3636-3641.Google Scholar
  25. 25.
    YANG Jun, LIU Pei, HU Wenping Method for Predicting Failure Rate of Power Transmission Equipment Based on Cloud Theory [J]High Voltage Engineering2014, (08): 2321-2327.Google Scholar
  26. 26.
    HU Jun, CAI Jingwen, HE Jinliang Factors Impacting Self-healing Overvoltage of Smart Distribution Network Connected with Micro grids[J]High Voltage Engineering2014, (05): 1559-1566.Google Scholar

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

Personalised recommendations