Optimal Placement Strategy of Distributed Generators based on Radial Basis Function Neural Network in Distribution Networks

In order to minimize the power losses and improve the voltage profile of the distribution network, introduction of production decentralized, also called decentralized generators or distributed generators (DG), in distribution network plays an important role. The optimal placement and sizing of DG is necessary for profound DG potential. To solve this combinatorial problem, a radial basis function neural network based optimization technique is proposed in this paper. Training and Learning of the Neural Network (NN) has profuse importance while dealing with the real problems, however, the computation time associated with the progression put a question mark on the effectiveness of the approach. RBFNN with conventional learning algorithms does not achieve the desired speed and performance in the training process. To overcome this difficulty a heuristic technique Particle Swarm Optimization (PSO) is employed for efficient learning of the RBFNN. Network tested on IEEE-69bus system is used to evaluate the effectiveness of this method. The convergence of RBFNN-PSO is compared with Conventional RBFNN.