Neural networks include the capacity to map the perplexed & extremely non – linear relationship between load levels of zone and the system topologies, which is required for the feeder reconfiguration in distribution systems. This study is intended to purpose the strategies to reconfigure the feeders by using artificial neural n/w s with the mapping ability. Artificial neural n/w’s 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 system topology which is provided by artificial neural networks.
Artificial neural networks determine the most appropriate system topology according to the load pattern on the basis of trained knowledge in the training set . This is contrary to the repetitive process of transferring the load & estimating power loss in conventional algorithm.
ANN are designed to two groups:
1) The first group is to estimate the proper load data of each zone .
2)The second is to determine the appropriate system topology from input load level .
In addition, several programs with the training set builder are developed for the design the training & accuracy test of A.N.N.
This paper will present the strategy of feeder reconfiguration to reduce power loss, by using A.N.N. The approach developed here is basically different from methods reviewed above on flow solution during search process are not required .The training set of A.N.N is the optimal system topology corresponding to various load patterns which minimizes the loss under given conditions.