A Backend Framework for the Efficient Management of Power System Measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.Comment: Published in Electric Power Systems Research (2016), not available ye

Event Detection Using Correlation within Arrays of Streaming PMU Data

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

Power System Spoof Detection with a Hybrid Hardware/Software Benchmarking Framework

The integration of monitoring and control networks at different voltage levels and across utility boundaries has made it harder to maintain and assess 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