Name
Abstract | ||
|---|---|---|
Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions and Gaussian injections in particular. Moreover, our algorithm applies to the practical case of unbalanced three-phase power flow. The performance is validated on AC power flow simulations over three phase IEEE distribution grid test cases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/tpwrs.2019.2897004 | IEEE Transactions on Power Systems |
Field | DocType | Volume |
Topology,Random variable,Control theory,Conditional independence,Electric power system,AC power,Probability distribution,Low voltage,Graphical model,Mathematics,Grid | Journal | abs/1803.06531 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deepjyoti Deka | 1 | 68 | 16.63 |
| Michael Chertkov | 2 | 465 | 59.33 |
| Scott Backhaus | 3 | 112 | 20.95 |