Making grids smarter

New algorithms for diagnosing line failures in a smart grid.

Summary

The power grid is changing, driven by many factors: the push to increased renewable energy sources, highly dynamic power markets, and deployment high-resolution measurements of transmission networks by syncrophasor measurement units (PMUs). How can computer science help operators make well-informed decisions in this increasingly dynamic and data-rich environment? As part of a larger effort at Cornell focused on putting the smarts into smart grids, we are looking into ways to mine the data from PMUs and related sensors in order to quickly determine when and where power lines are failing – before an irate customer calls to complain about an outage.

Papers

C. Ponce and D. Bindel, “FLiER: Practical Topology Update Detection Using Sparse PMUs,” Jul. 2016. Accepted by IEEE Transactions on Power Systems. 
@techreport{2016-flier-tr,
  author = {Ponce, Colin and Bindel, David},
  title = {{FLiER}: Practical Topology Update Detection Using Sparse {PMU}s},
  month = jul,
  year = {2016},
  arxiv = {1409.6644},
  link = {http://arxiv.org/pdf/1409.6644v3},
  code = {https://github.com/cponce512/FLiER_Test_Suite/},
  status = {unrefereed},
  submit = {Accepted by IEEE Transactions on Power Systems.}
}

Abstract:

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.

Talks

Fast Fingerprints for Power System Events

Lawrence Berkeley Lab
grid • seminar external invited

Fast Fingerprints for Power System Events

CompSustNet 2016 Workshop, Cornell University
grid • meeting local

FLiER: Practical Topology Error Correction Using Sparse PMUs

ARPAe Innovation Summit, National Harbor, MD
grid • meeting external poster