Artificial neural-network based feeder reconfiguration for loss reduction in distribution systems

TLDR: Strategies are proposed to reconfigure the feeder in distribution systems by using artificial neural networks (ANNs) with mapping ability to reduce the power loss according to the variation of load pattern.

Abstract: Strategies are proposed to reconfigure the feeder in distribution systems by using artificial neural networks (ANNs) with mapping ability. ANNs determine the appropriate system topology that reduces the power loss according to the variation of load pattern. The control strategy can be easily obtained on the basis of the system topology which is provided by ANNs. ANNs are designed in two groups. The first group estimates the proper load level from the load data of each zone. The second determines the appropriate system topology from the input load level. Several programs with the training set builder are developed for the design, the training, and the accuracy test of artificial neural networks. The performance of neural networks designed is evaluated on the test distribution system. Neural networks are implemented in FORTRAN language and trained on a 386 PC. >