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Power IT Lab » Research

Measurement Data-driven Model for Large Grid

Oak Ridge naional Laboratory

Data Driven Model

Fig. 1. Comparison of Estimated Frequency Response and Actual Frequency Measurement

Traditional power system dynamic models are very complex due to the intricacy of power system networks and can be inaccurate for control because of the amount of details needed and the fact that the grid topology changes all the time. With the fast deployment of a large number of Phasor Measurement Units (PMUs) in the transmission network, a purely measurement-based model for large grid dynamics estimation and control may be obtained.

This project proposed a measurement-based power system dynamic modeling method for system response estimation and instability warning, which is based on a reduced dynamic power grid model in the autoregressive with exogenous input model structure. For a large-scale power grid, the identification of the proposed model is computationally efficient and suitable for online applications. Case studies have been conducted to test the proposed modelís accuracy and reliability with different excitation sources for model training and cascading events leading to out of step. The preliminary results have shown that the proposed method is effective in estimating power system dynamic response from limited synchrophasor measurements and promising in predicting instability for out of step or other instability issues caused by cascading outages. Future work will investigate how to improve the accuracy of the proposed method and theoretically prove its effectiveness in instability warning. Although further investigation continues, it seems that based on the proposed method, online synchrophasor-based power system monitoring and decision support applications may be developed for power system response estimation under disturbances and for early warning of potential stability issues.


[1] Yong Liu; Kai Sun; Yilu Liu, "Measurement-based power system dynamic model for response estimation," Power and Energy Society General Meeting, 2012 IEEE , vol., no., pp.1,6, 22-26 July 2012.

[2] Changgang Li; Yong Liu; Kai Sun; Yilu Liu; Navin Bhatt, "Measurement Based Power System Dynamics Prediction with Multivariate AutoRegressive Model", IEEE PES Transmission & Distribution Conference & Exposition, 2014 IEEE, vol., no., pp.1,6, May 2014.

[3] Feifei Bai; Yong Liu; Yilu Liu, et al, "Measurement-Based Correlation Approach for Power System Dynamic Response Estimation," IET Generation, Transmission & Distribution, to be published.