Power distribution network reconfiguration for power loss minimization using novel dynamic fuzzy c-means (dFCM) clustering based ANN approach

•A novel frame work is proposed to reconfigure power distribution networks.•The frame work is obtained using a proposed ANN and dFCM clustering algorithm.•Very short process time that is far shorter than the classic method.•Very simple structure including only a minimal number of neurons.

In this study, a three-layer artificial neural network (ANN) is proposed to reconfigure power distribution networks to obtain the optimal configuration in which the active power loss is minimal. Then, the proposed ANN is reduced in size by transforming the input space with kernels using a proposed modified dynamic fuzzy c-means (dFCM) clustering algorithm to obtain a novel framework. The proposed framework and ANN both are implemented on the two IEEE 33-bus and IEEE 69-bus power distribution networks. The ANN and framework both are trained using the training set consisting of only 64 training samples. The simulated results are compared to the results obtained by performing a selected traditional method which is the switching algorithm. The comparative results explicitly verify that using the proposed framework for distribution networks reconfiguration has some benefits such as a very short process time that is far shorter than the others, a very simple structure including only a minimal number of neurons and higher accuracy compared to the others. These features show that the proposed framework can be effectively used for real-time reconfiguration of power distribution networks.