Failure Localization in Power Systems via Tree Partitions
Abstract
Cascading failures in power systems propagate non-locally, making the control and mitigation of outages extremely hard. In this work, we use the emerging concept of the tree partition of transmission networks to provide an analytical characterization of line failure localizability in transmission systems. Our results rigorously establish the well perceived intuition in power community that failures cannot cross bridges, and reveal a finer-grained concept that encodes more precise information on failure propagations within tree-partition regions. Specifically, when a non-bridge line is tripped, the impact of this failure only propagates within well-defined components, which we refer to as cells, of the tree partition defined by the bridges. In contrast, when a bridge line is tripped, the impact of this failure propagates globally across the network, affecting the power flow on all remaining transmission lines. This characterization suggests that it is possible to improve the system robustness by temporarily switching off certain transmission lines, so as to create more, smaller components in the tree partition; thus spatially localizing line failures and making the grid less vulnerable to large-scale outages. We illustrate this approach using the IEEE 118-bus test system and demonstrate that switching off a negligible portion of transmission lines allows the impact of line failures to be significantly more localized without substantial changes in line congestion.
Additional Information
© 2018 is held by author/owner(s). This work has been supported by Resnick Fellowship, Linde Institute Research Award, NWO Rubicon grant 680.50.1529., NSF grants through PFI:AIR-TT award 1602119, EPCN 1619352, CNS 1545096, CCF 1637598, ECCS 1619352, CNS 1518941, CPS 154471, AitF 1637598, ARPA-E grant through award DE-AR0000699 (NODES) and GRID DATA, DTRA through grant HDTRA 1-15-1-0003 and Skoltech through collaboration agreement 1075-MRA.Attached Files
Published - p57-guo.pdf
Submitted - 1803.08551.pdf
Files
Name | Size | Download all |
---|---|---|
md5:0f00617c34e2ee6006da823e134dff1d
|
2.4 MB | Preview Download |
md5:730b23cabdbf8078f442f90418063fa0
|
2.0 MB | Preview Download |
Additional details
- Eprint ID
- 92490
- Resolver ID
- CaltechAUTHORS:20190128-094206311
- Resnick Sustainability Institute
- Linde Institute of Economic and Management Science
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)
- 680.50.1529
- NSF
- IIP-1602119
- NSF
- EPCN-1619352
- NSF
- CNS-1545096
- NSF
- CCF-1637598
- NSF
- ECCS-1619352
- NSF
- CNS-1518941
- NSF
- CPS-154471
- NSF
- CCF-1637598
- Advanced Research Projects Agency-Energy (ARPA-E)
- DE-AR0000699
- Defense Threat Reduction Agency (DTRA)
- HDTRA 1-15-1-0003
- Skoltech
- 1075-MRA
- Created
-
2019-01-29Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Resnick Sustainability Institute