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Phasor measurement units (PMUs) provide high-fidelity data that improvesituation awareness of electric power grid operations. PMU datastreams informwide-area state estimation, monitor area control error, and facilitate eventdetection in real time. As PMU data become more available and increasinglyreliable, these devices are found in new roles within control systems, such asremedial action schemes and early warning detection systems. As with othercyber physical systems, maintaining data integrity and security pose asignificant challenge for power system operators. In this paper, we present acomprehensive analysis of multiple machine learning techniques to detectmalicious data injection within PMU data streams. The two datasets used in thisstudy come from two PMU networks: an inter-university, research-gradedistribution network spanning three institutions in the U.S. Pacific Northwest,and a utility transmission network from the Bonneville Power Administration. Weimplement the detection algorithms with TensorFlow, an open-source softwarelibrary for machine learning, and the results demonstrate potential fordistributing the training workload and achieving higher performance, whilemaintaining effectiveness in the detection of spoofed data.

Defending Against Adversarial Attacks in Transmission- and Distribution-level PMU Data