Name
Abstract | ||
|---|---|---|
This paper considers a probabilistic generalization of the $N$-$k$ failure-identification problem in power transmission networks, where the probability of failure of each component in the network is known a priori and the goal of the problem is to find a set of $k$ components that maximizes disruption to the system loads weighted by the probability of simultaneous failure of the $k$ components. The resulting problem is formulated as a bilevel mixed-integer nonlinear program. Convex relaxations, linear approximations, and heuristics are developed to obtain feasible solutions that are close to the optimum. A general cutting-plane algorithm is proposed to solve the convex relaxation and linear approximations of the $N$-$k$ problem. Extensive numerical results corroborate the effectiveness of the proposed algorithms on small-, medium-, and large-scale test instances, the test instances include the IEEE 14-bus system, the IEEE single-area and three-area RTS96 systems, the IEEE 118-bus system, the WECC 240-bus test system, the 1354-bus PEGASE system, and the 2383-bus Polish winter-peak test system. |
Year | Venue | Field |
|---|---|---|
2018 | Networks | Flow network,Probabilistic-based design optimization,Mathematical optimization,Nonlinear system,Nonlinear programming,Electric power system,Probabilistic analysis of algorithms,Heuristics,Probabilistic logic,Mathematics |
DocType | Volume | Issue |
Journal | 71 | 3 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaarthik Sundar | 1 | 75 | 11.68 |
| Carleton Coffrin | 2 | 204 | 20.20 |
| H. Nagarajan | 3 | 48 | 9.37 |
| Russell Bent | 4 | 79 | 15.68 |