This research is supported by student scholarships from Oregon BEST and the Bonneville Power Administration.
Electric power transmission, Electric power systems -- Mathematical models -- Analysis, Wide area networks (Computer networks) -- Control
Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of "if" but a matter of "when" in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.
Landford, J., Meier, R., Barella, R., Zhao, X., Cotilla-Sanchez, E., Bass, R. B., & Wallace, S. (2015). Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks. arXiv preprint arXiv:1509.05086.