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
论文数: 0引用数: 0
h-index: 0
机构:
Univ Sao Paulo, Escola Politecn, Sao Paulo, BrazilUniv Sao Paulo, Escola Politecn, Sao Paulo, Brazil
Damato Kornrumpf, Luiz Henrique
[1
]
Nabeta, Silvio Lkuyo
论文数: 0引用数: 0
h-index: 0
机构:
Univ Sao Paulo, Escola Politecn, Sao Paulo, BrazilUniv Sao Paulo, Escola Politecn, Sao Paulo, Brazil
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.