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

机译：排除保险丝链路的神经模型用于计算机辅助应用的时间-电流特性

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摘要

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 (TM) 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.
机译：由于其简单性和低成本，驱除熔断器是架空配电系统中最常用的保护设备之一。关于配电保护方案的许多已发表的研究都在思考这些设备的存在。这些研究在需要执行计算仿真时通过数字模型表示保险丝元件。本文的目的是通过人工神经网络（ANN）对驱逐熔断器的时间-电流特性（TCC）进行建模。为了实现此目标，使用最小二乘法来评估所获得的结果，并将残差平方和（RSS）用作比较参数。使用此参数的目的是在考虑电流，速度和制造商的不同额定值的情况下，确定最适合全球范围的模型。该方法在IEEE 34节点径向测试馈线上进行了测试。该网络是使用DIgSILENT PowerFactory（TM）软件构建的。仿真结果表明，与传统的双指数函数和多项式函数相比，所提出的人工神经网络方法为熔丝曲线的拟合提供了一种鲁棒且准确的技术。

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