Name
Abstract | ||
|---|---|---|
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new “Learning-to-Infer” method is developed for efficient inference of every line status in the network. Optimizing the line outage detector is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount rapidly and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time multi-line outage identification. The proposed methods are evaluated in the IEEE 30, 118, and 300 bus systems. Excellent performance in identifying multi-line outages in real time is achieved with a reasonably small amount of data. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TSG.2019.2925405 | IEEE Transactions on Smart Grid |
Keywords | DocType | Volume |
Mathematical model,Power measurement,Real-time systems,Voltage measurement,Network topology,Topology,Predictive models | Journal | 11 |
Issue | ISSN | Citations |
1 | 1949-3053 | 2 |
PageRank | References | Authors |
0.72 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Zhao | 1 | 13 | 8.01 |
| Jianshu Chen | 2 | 883 | 52.94 |
| H. V. Poor | 3 | 25411 | 1951.66 |