Neural model of the expulsion fuse link Time–Current Characteristic for computer-aided applications

Abstract

Due to its simplicity and low cost, one of the most common protection devices used in overhead distribution systems is the expulsion fuse links. Numerous published studies regarding distribution protection schemes ponder the existence of these devices. These studies represent the fuse element through digital models, when it is necessary to perform computational simulations. The purpose of the present paper is to model the Time–Current Characteristics (TCC) of expulsion fuse links through an Artificial Neural Network (ANN). To accomplish this objective, the Least Square Method was used in order to evaluate the results obtained, and the Residual Sum of Square (RSS) was used as a comparative parameter. The objective of using this parameter is to identify the models that best fit in the global scope, taking into account the different nominal ratings of current, speed rates and manufacturers. The proposed method was tested on an IEEE 34 node radial test feeder. The network was built using DIgSILENT PowerFactory™ software. The simulation results show that the proposed ANN method provides a robust and accurate technique for fuse link curve fitting when compared with the traditional double exponential and polynomial functions.