Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems
Journal Article
·
· IEEE Transactions on Power Delivery (Institute of Electrical and Electronics Engineers); (United States)
OSTI ID:6093932
- Korea Electrotechnology Research Inst., Changwon (Korea, Republic of). Dept. of Distribution System
Neural networks have the capability to map the complex and extremely non-linear relationship between the load levels of zone and system topologies, which is required for feeder reconfiguration in distribution systems. This study is intended to propose the strategies to reconfigure the feeder, by using artificial neural networks with mapping ability. Artificial neural networks determine the appropriate system topology that reduces the power loss according to the variation of load pattern. The control strategy can be easily obtained from the system topology which is provided by artificial neural networks. Artificial neural networks are in groups. The first group estimates the proper load level from the load data of each zone, and the second determines the appropriate system topology from the input load level. In addition, several programs with the training set builder are developed for the design, the training and the accuracy test of artificial neural networks. The authors also evaluate the performance of neural networks designed here, on the test distribution system. Neural networks are implemented in FORTRAN language, and trained on the personal computer COMPAQ 386.
- OSTI ID:
- 6093932
- Journal Information:
- IEEE Transactions on Power Delivery (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Power Delivery (Institute of Electrical and Electronics Engineers); (United States) Vol. 8:3; ISSN 0885-8977; ISSN ITPDE5
- Country of Publication:
- United States
- Language:
- English
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