Data-driven ambient noise correlation for characterization and fragility evaluation in power grids
Qianli Chen, & Ahmed E. ElbannaPublished August 3, 2016, SCEC Contribution #6491, 2016 SCEC Annual Meeting Poster #182
The response of ambient noise has been used to cross correlate and extract the Green’s function for general hyperbolic PDE’s in many fields including acoustics, seismology and helioseismology. The equipartitioning of wave field enable the successful retrieval of the Green’s function of system with wave-like solution. Recently, researchers found out that the electrical power network consisting of transmission lines, generators, and loads can be described by the propagation of electromechanical (EM) waves in terms of the flow of power load and the inertia of the synchronous generators. Utilizing the method of retrieval the Green’s function from cross correlation and the approach of PDE description of power grid system, we can extract the Green’s functions for pairs of electrical generators and predict the influence of potential disturbance from one power station on the others. We apply this method first to a synthetic but realistic power gird ambient frequency data using IEEE standard test cases and real grid models for EI and WECC systems available at UIUC. After comparing the cross-correlation with the Green’s function and validating the results, we apply the method to real PMU datasets. By building up this framework, we are able to continue further work in the area of synchrophasor data analytics as well as enhance the resilience improvements against cyber-physical attacks.
Citation
Chen, Q., & Elbanna, A. E. (2016, 08). Data-driven ambient noise correlation for characterization and fragility evaluation in power grids. Poster Presentation at 2016 SCEC Annual Meeting.
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