A Radial Basis Function Neural Network based Optimal Placement of Distributed generation sources in Distribution Networks

Distributed Generation (DG) has been envisaged to play an escalating role in electrical power system in near future. However, the benefits of DGs cannot be explored if they are not optimally placed. The efficiency of the distribution system is increased units. As the employment of the DG comes with the enhancement of the distribution feeders. Network (RBFNN) based strategy to penetrate the Radial Distribution Network. Training and learning of the Neural Network (NN) has an ample importance while dealing with the real problems, however, the computation time associated with the process put a question mark on the effectiveness of the learning algorithms does not achieve the desired speed and performance in the training process. To overcome this difficulty a heuristic technique for efficient learning of the RBFNN. The convergence of RBFNN Conventional RBFNN. Efficacy of the proposed approach is tested over the standard IEEE system.