FLiER: Practical Topology Update Detection Using Sparse PMUs
For power grid operators, FLiER enables rapid topology change detection with limited sensor coverage, improving situational awareness.
FLiER detects topology changes in power networks using sparse PMU data, achieving faster identification than brute-force with minimal accuracy loss; e.g., on IEEE 57-bus, it identifies line outages accurately.
In this paper, we present a Fingerprint Linear Estimation Routine (FLiER) to identify topology changes in power networks using readings from sparsely-deployed phasor measurement units (PMUs). When a power line, load, or generator trips in a network, or when a substation is reconfigured, the event leaves a unique "voltage fingerprint" of bus voltage changes that we can identify using only the portion of the network directly observed by the PMUs. The naive brute-force approach to identify a failed line from such voltage fingerprints, though simple and accurate, is slow. We derive an approximate algorithm based on a local linearization and a novel filtering approach that is faster and only slightly less accurate. We present experimental results using the IEEE 57-bus, IEEE 118-bus, and Polish 1999-2000 winter peak networks.