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)|
|Number of pages||4|
|Journal||Proceedings - IEEE International Symposium on Circuits and Systems|
|State||Published - Dec 1 1989|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering