Moghavvemi, M. (1999) Sectionalized ANN approach in predicting voltage stability in power systems. International Journal of Power and Energy Systems, 19 (1). pp. 66-70. ISSN 10783466,
Full text not available from this repository.Abstract
In recent years artificial neural networks (ANNs) have been proposed as an alternative method for solving certain difficult power system problems for which conventional techniques have not achieved the desired speed, accuracy, or efficiency. ANN methodology allows complex relationships between an initial state and a final state to be determined by an iterative mathematical algorithm, instead of by an expert. A properly trained ANN can classify the security of a previously unencountered input pattern with good accuracy. As systems grow in size and complexity, the mapping to be learned become increasingly complicated. In this paper the use of a sectionalized ANN approach is proposed for predicting the voltage stability index of a large-scale power system.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Power system stability; Sectionalized artificial neural network; Voltage collapse indicator; Voltage stability; Algorithms; Backpropagation; Electric breakdown; Electric power systems; Indicators (instruments); Learning systems; Neural networks; System stability; Electric potential |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Norhamizah Tamizi |
| Date Deposited: | 21 Mar 2014 02:57 |
| Last Modified: | 23 Nov 2017 03:04 |
| URI: | http://eprints.um.edu.my/id/eprint/9680 |
Actions (login required)
 |
View Item |
