典型文献
Designing Evolutionary Wavelet Neural Network for Estimating Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices
文献摘要：
In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets.For this purpose,all of the electrical parameters for six melting processes are measured with a power quality analyzer,attached to the secondary com-ponent of an EAF transformer at a Saba steel complex,to estimate the foaming slag quality.Experimental results on various combinations of measured electrical parameters,applying the designed EWNN estimator,demonstrate that utilizing five leading indicators leads to the highest precision.The obtained 99％accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.
文献关键词：
中图分类号：
作者姓名：
Hamzeh Rezvani;Hamed Khodadadi
作者机构：
Department of Electrical Engineering,Khomeinishahr Branch,Islamic Azad University,Isfahan,Iran
文献出处：
引用格式：
[1]Hamzeh Rezvani;Hamed Khodadadi-.Designing Evolutionary Wavelet Neural Network for Estimating Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices)[J].中国电机工程学会电力与能源系统学报（英文版）,2022(04):1165-1174
A类：
EWNN,Saba
B类：
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AB值：
0.588174
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