Distribution feeder loss analysis by using an artificial neural network

This paper proposes an artificial neural network (ANN) based feeder loss analysis for distribution system analysis. The functional-link network model is examined to form the artificial neural network architecture to derive various loss calculation models for distribution feeders with different configurations. The ANN is a feedforward network that uses a standard back-propagation algorithm to adjust the weights on the connection path between any two processing elements. The typical daily load curve of the study feeder for each season is derived to field test data. A three-phase load flow program is then executed to create the ANN training sets to solve the exact feeder loss. A sensitivity analysis is performed to determine the key factors of feeder loss, which are feeder loading and power factor, primary and secondary conductor length, and transformer capacity. The above key factors form the variables of the ANN input layer. By applying the artificial neural network with pattern recognition capability, this study has developed the seasonal loss calculation models for both an overhead and an underground distribution feeder. Two practical feeders in the Taiwan Power Company (Taipower) distribution system have been selected for computer simulation to demonstrate the effectiveness and accuracy of the proposed ANN loss models. By comparing the loss models derived by the conventional regression technique, it is found that the proposed loss models can estimate feeder loss in a very effective manner and provide a better tool for distribution engineers to enhance system operation efficiency.