Rapid grid state estimation using Singular Value Decomposition similarity matching

This research investigates the usage of data clustering and similarity matching techniques as a means for real-time electricity grid state estimation. Real-time grid estimation using real-time Phasor Measurement Unit (PMU) data can enable next-generation grid monitoring, control, and protection schemes. However, PMUs are typically installed at only a few of the hundreds of buses of any substantial network. This research leverages a Singular Value Decomposition (SVD) technique used to identify trends and similarities in large data sets as a means to identify PMU measurements and grid states that are similar and can thus be used to estimate the state of unobserved buses. The presented technique is modular and fast. Once the initial decomposition is completed, additional calculations require only a few simple matrix operations and can thus be carried out in real-time or near real-time. Two systems are utilized to test and demonstrate the technique: the IEEE 14-bus system and a 286-bus system based on the Oregon State University (OSU) campus. The results show that in the case of the IEEE 14-bus system, with 3 out of 14 buses directly observed, the entire system state estimation error is between 5 percent and 13 percent. For the OSU 286-bus system, with 3 out of 286 buses observed, estimation errors of 3 percent to 20 percent are demonstrated. SVD similarity matching techniques have promising applications to grid state estimation problems in which speed and sparseness are key.