Abstract
The utility of trained neural networks in calculating the network state and classifying its security status under different load and contingency conditions is demonstrated. In particular, a two-layer multiperceptron is used to screen contingent branch overloads. The performance of this approach is evaluated using a six-bus example. The results indicate that the proposed tasks can be performed reliably by back-propagation-trained multiperceptrons.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 486-489 |
| Number of pages | 4 |
| Journal | Proceedings - IEEE International Symposium on Circuits and Systems |
| Volume | 1 |
| State | Published - 1989 |
| Externally published | Yes |
| Event | IEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1 - Portland, OR, USA Duration: May 8 1989 → May 11 1989 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering