| dc.creator |
IŞIK, ALİ HAKAN |
|
| dc.creator |
GÜVENÇ, UĞUR |
|
| dc.creator |
AKKAYA, İBRAHİM |
|
| dc.creator |
YİĞİT, Tuncay |
|
| dc.date |
2015-12-31T22:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:29:27Z |
|
| dc.date.available |
2020-10-06T09:29:27Z |
|
| dc.identifier |
1b9f2a2e-2880-42fb-b560-55b50b4e4d94 |
|
| dc.identifier |
10.3906/elk-1311-111 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/1b9f2a2e-2880-42fb-b560-55b50b4e4d94/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/54646 |
|
| dc.description |
A self-tuning method to determine the appropriate parameters of a proportional-integral-derivative controller for an automatic voltage regulator (AVR) system using a biogeography-based optimization (BBO) algorithm is proposed in this study. The BBO algorithm was developed based on the theory of biogeography, which describes migration and its results. We propose that the BBO algorithm has a high-quality solution and stable convergence characteristics, and thus it improves the transient response of the controlled system. The performance of the BBO algorithm depends on the transient response, root locus, and Bode analysis. Robustness analysis is done in the AVR system, which is tuned by an artificial bee colony (ABC) algorithm in order to identify its response to changes in the system parameters. We compare the BBO algorithm with the ABC algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The results of this comparison show that the BBO algorithm has a better tuning capability than the other optimization algorithms. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Performance analysis of biogeography-based optimization for automatic voltage regulator system |
|
| dc.type |
info:eu-repo/semantics/article |
|