TY - JOUR
T1 - Predicting failures in power grids
T2 - The case of static overloads
AU - Chertkov, Michael
AU - Pan, Feng
AU - Stepanov, Mikhail G.
N1 - Funding Information:
Manuscript received June 24, 2010; revised September 15, 2010; accepted October 27, 2010. Date of publication December 10, 2010; date of current version February 18, 2011. The work at Los Alamos National Laboratory was carried out under the auspices of the National Nuclear Security Administration of the U.S. Department of Energy at Los Alamos National Laboratory under Contract No. DE C52-06NA25396. This work was supported in part by DTRA/DOD under Grant BRCALL06-Per3-D-2-0022 on “Network Adaptability from WMD Disruption and Cascading Failures.” The work of M. Chertkov was supported in part by NMC via the NSF collaborative grant CCF-0829945 on “Harnessing Statistical Physics for Computing and Communications.” Paper no. TSG-00084-2010.
PY - 2011/3
Y1 - 2011/3
N2 - Here we develop an approach to predict power grid weak points, and specifically to efficiently identify the most probable failure modes in static load distribution for a given power network. This approach is applied to two examples: Guam's power system and also the IEEE RTS-96 system, both modeled within the static dc power flow model. Our algorithm is a power network adaption of the worst configuration heuristics, originally developed to study low probability events in physics and failures in error-correction. One finding is that, if the normal operational mode of the grid is sufficiently healthy, the failure modes, also called instantons, are sufficiently sparse, i.e., the failures are caused by load fluctuations at only a few buses. The technique is useful for discovering weak links which are saturated at the instantons. It can also identify generators working at the capacity and generators under capacity, thus providing predictive capability for improving the reliability of any power network.
AB - Here we develop an approach to predict power grid weak points, and specifically to efficiently identify the most probable failure modes in static load distribution for a given power network. This approach is applied to two examples: Guam's power system and also the IEEE RTS-96 system, both modeled within the static dc power flow model. Our algorithm is a power network adaption of the worst configuration heuristics, originally developed to study low probability events in physics and failures in error-correction. One finding is that, if the normal operational mode of the grid is sufficiently healthy, the failure modes, also called instantons, are sufficiently sparse, i.e., the failures are caused by load fluctuations at only a few buses. The technique is useful for discovering weak links which are saturated at the instantons. It can also identify generators working at the capacity and generators under capacity, thus providing predictive capability for improving the reliability of any power network.
KW - Distance to failure
KW - power flow
KW - rare events
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U2 - 10.1109/TSG.2010.2090912
DO - 10.1109/TSG.2010.2090912
M3 - Article
AN - SCOPUS:79951954598
SN - 1949-3053
VL - 2
SP - 162
EP - 172
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 1
M1 - 5661887
ER -