Biblio

Phasor measurement units (PMUs) provide high-fidelity situational awareness of electric power grid operations. PMU data are used in real-time to inform wide area state estimation, monitor area control error, and event detection. As PMU data becomes more reliable, these devices are finding roles within control systems such as demand response programs and early fault detection systems. As with other cyber physical systems, maintaining data integrity and security are significant challenges for power system operators. In this paper, we present a comprehensive study of multiple machine learning techniques for detecting malicious data injection within PMU data streams. The two datasets used in this study are from the Bonneville Power Administration's PMU network and an inter-university PMU network among three universities, located in the U.S. Pacific Northwest. These datasets contain data from both the transmission level and the distribution level. Our results show that both SVM and ANN are generally effective in detecting spoofed data, and TensorFlow, the newly released tool, demonstrates potential for distributing the training workload and achieving higher performance. We expect these results to shed light on future work of adopting machine learning and data analytics techniques in the electric power industry.