Distribution feeder loss computation by artificial neural network

This paper proposes an artificial neural network (ANN) based feeder loss calculation model for distribution system analysis. In this paper, the functional-link network model is examined to form the artificial neural network architecture to derive the various loss calculation models for feeders with different configuration. Such an artificial neural network is a feedforward network that uses a standard back-propagation algorithm to adjust weights on the connection path between any two processing elements (PEs). Feeder daily load curve in each season are derived by field test data. A three-phase load flow program is executed to create the training sets with exact loss calculation results. A sensitivity analysis is executed to determine the key factors including power factor, feeder loading primary conductors, secondary conductors, and transformer capacity as the variables for components located at the input layer. By using an artificial neural network with pattern recognition ability, this study has developed seasonal and yearly loss calculation models for overhead and underground feeder configurations. Two practical feeders with both overhead and underground configurations in the Taiwan Power Company distribution system are selected for computer simulation to demonstrate the effectiveness and accuracy of the proposed models. Compared with models derived by the conventional regression technique, results indicate that the proposed models provide more efficient tools to the district engineer for feeder loss calculation