HYBRID APPROACH USING COUNTERPROPAGATION NEURAL-NETWORK FOR POWER-SYSTEM NETWORK REDUCTION

Citation
Kl. Lo et al., HYBRID APPROACH USING COUNTERPROPAGATION NEURAL-NETWORK FOR POWER-SYSTEM NETWORK REDUCTION, IEE proceedings. Generation, transmission and distribution, 144(2), 1997, pp. 169-174
Citations number
20
Categorie Soggetti
Engineering, Eletrical & Electronic
ISSN journal
13502360
Volume
144
Issue
2
Year of publication
1997
Pages
169 - 174
Database
ISI
SICI code
1350-2360(1997)144:2<169:HAUCNF>2.0.ZU;2-E
Abstract
A hybrid counterpropagation neural network and Ward-type equivalent ap proach for power system network reduction is proposed for improving th e conventional external system equivalent technique. The proposed Ward -type equivalent technique not only possesses the good properties of t he extended Ward equivalent, but can also update the parameters of the equivalent model for representing real-time topology changes of the e xternal system. Another improvement is that a counterpropagation neura l network is used to match the boundary equivalent power injections, T he new hybrid approach combines the simplicity of Ward-type equivalent techniques with the speed of artificial neural networks. Test results demonstrate that the hybrid approach and highly accurate is very effi cient and compared to the external system equivalent.