Event Detection Using Correlation within Arrays of Streaming PMU Data

This thesis provides a synchrophasor data analysis methodology that leverages both statistical correlation techniques and a statistical distribution in order to identify data inconsistencies, as well as power system contingencies. This research utilizes archived Phasor Measurement Unit (PMU) data obtained from the Bonneville Power Administration in order to show that this methodology is not only feasible, but extremely useful for power systems monitoring, decision support, and planning purposes. By analyzing positive sequence voltage angles between a pair of PMUs at two different substation locations, an historic record of correlation is established. From this record, a Rayleigh distribution of correlation coefficients is calculated. The statistical parameters of this Rayleigh distribution are used to infer occurrences of power system and data events. To monitor an entire system, a simple solution would be observing each of these parameters for every PMU combination. One issue with this approach is that correlation of some PMU pairs may be redundant or yield little value to monitoring capabilities. Additionally, this approach quickly encounters scalability issues as each additional PMU adds considerably to computation - for example, if the system contains n PMUs the amount of computations will be n(n-1)/2. System-wide monitoring of these parameters in this fashion is cumbersome and inefficient. To address these issues, an alternative scheme is proposed which involves monitoring only a subset of PMUs characterized by electrically coupled zones, or clusters, of PMUs. These clusters include both electrically-distant and electrically-near PMU sites. When monitored over an event, these yield statistical parameters sufficient for detecting event occurrences. This clustering scheme can be utilized to significantly decrease computation time and allocation of resources while maintaining optimal system observability. Results from the statistical methods are presented for a select few case studies for both data and power system event detection. In addition, determination of cluster size and content is discussed in detail. Lastly, the viability of monitoring pertinent statistical parameters over various clustering schemes is demonstrated

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

Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks

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.Comment: 8 pages, 4 figures, submitted to IEEE Transaction

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