Conference proceeding
Screening power system contingencies using a back-propagation trained multiperceptron
1989 IEEE International Symposium on Circuits and Systems (ISCAS), pp 486-489 vol.1
1989
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.< >
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Details
- Title
- Screening power system contingencies using a back-propagation trained multiperceptron
- Creators
- R Fischl - Drexel UniversityM Kam - Drexel UniversityJ.-C Chow - Drexel UniversityS Ricciardi - Drexel University
- Publication Details
- 1989 IEEE International Symposium on Circuits and Systems (ISCAS), pp 486-489 vol.1
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Other Identifier
- 991019346722704721