Phasor measurement units provide real-time power system monitoring. We present a data analysis method that leverages statistical correlation and analysis methods to identify power system events. This research uses archived phasor measurement unit data to show that the method is useful for detecting power system events. Results from a lighting strike case study are presented. A monitoring stratagem based on PMU clustering is discussed, and the viability of monitoring pertinent statistical parameters over various clustering schemes is demonstrated.
The integration of monitoring and control networks at different voltage levels and across utility boundaries has made it harder to maintain and asses the resilience of power systems due to increasing cyber attacks. On the software side, a variety of research efforts pursue cyber protection algorithms, such as spoof detection techniques. On the hardware and firmware side, research has demonstrated the feasibility of adversarial attacks by providing an entry point at the device level. This work proposes and evaluates two detection performance metrics for a variety of cyber spoofing attacks introduced in a realistic Phasor Measurement Unit (PMU) network for a hybrid transmission and distribution power system. This research finds that both proposed metrics show promise in aiding a spoof detection algorithm in consistently detecting spoofs in power system measurements.
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