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

•An neural network model for expulsion fuse link Time–Current Characteristic is proposed.•Validation is done considering five important fuse manufacturers.•Only preferred “K” and “H” fuse links were tested, but the proposed technique can be apply to any kind of fuse link.•The proposed method provide...

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Bibliographic Details
Published in:Electric power systems research 2019-10, Vol.175, p.105899, Article 105899
Main Authors: da Costa, Guilherme Braga, Bertoletti, Augusto Z., Morais, Adriano P., Junior, Ghendy C., Oliveira, Aécio L.
Format: Article
Language:English
Subjects:
Artificial Neural Network
Artificial neural networks
Computer simulation
Curve fitting
Electricity distribution
Expulsion
Expulsion fuse links
Functions (mathematics)
Neural networks
Parameter identification
Polynomials
Simulation
Surge protectors
Time–Current Characteristic
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Summary:•An neural network model for expulsion fuse link Time–Current Characteristic is proposed.•Validation is done considering five important fuse manufacturers.•Only preferred “K” and “H” fuse links were tested, but the proposed technique can be apply to any kind of fuse link.•The proposed method provides a robust and accurate technique for expulsion fuse link. 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.
ISSN:0378-7796
1873-2046
DOI:10.1016/j.epsr.2019.105899