Load Flow Analysis of Distribution System Using Artificial Neural Networks

In distribution system to determine static states at each node or bus and operating conditions, the load flow studies are very crucial. The load flow studies are very important, not only in finding static states but also during distribution system planning and its extension. In this paper, the load flow problem has been solved by artificial neural networks and these networks are efficient to describe the relation involved within the raw data. Two types neural networks are proposed to solve load flow problem of a distribution system, first one is Radial Basis Function Neural Network (RBFN) and other one is Multilayer Feedforward Neural Network with Backpropagation Algorithm (MFFN with BPA). The mathematical model of distribution load flow comprises a set of nonlinear algebraic equations that are solved using network topology-based distribution load flow which is usurped as reference off-line load flow. A series of training data is generated using off-line load flow, which is used to train the neural networks. The training data consists of different loading conditions and voltages corresponding to each and every node in the distribution system. The neural networks are trained with series of training data and tested with a loading which is not present in training data. Results obtained from two neural networks closely agrees with the reference off-line load flow result of same loading. The results of neural networks are compared together and computational time of two neural networks is considerably small.