Determination of synchronous machine parameters through the SSFRT<?show [AQ ID=Q1]?> test and artificial neural networks

被引:0
|
作者
Damato Kornrumpf, Luiz Henrique [1 ]
Nabeta, Silvio Lkuyo [1 ]
机构
[1] Univ Sao Paulo, Escola Politecn, Sao Paulo, Brazil
来源
JOURNAL OF ENGINEERING-JOE | 2019年 / 17期
关键词
neural nets; frequency response; synchronous generators; electric machine analysis computing; machine testing; SSFRT test; artificial neural networks; frequency response test; frequency tests; low-cost equipment; neural network optimisation process; synchronous machine parameter determination;
D O I
10.1049/joe.2018.8141
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The frequency response test on synchronous generators has been increasing in the last decades, but the high cost of equipment used for conducting the test is still a stumbling block for both manufacturers and end consumers. This study aims to propose a methodology for obtaining parameters, through the use of neural networks. This study treats the results designed through frequency tests, in which the proposal was the use of low-cost equipment to perform them. In addition, a process of optimisation of the neural network was developed during the development of this study.
引用
收藏
页码:4576 / 4579
页数:4
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