Open AccessDetermination of synchronous machine parameters through the SSFRTtest and artificial neural networks
Author(s) -
Kornrumpf Luiz Henrique Damato,
Nabeta Silvio Ikuyo
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8141
Subject(s) - artificial neural network , computer science , block (permutation group theory) , process (computing) , test (biology) , artificial intelligence , machine learning , control engineering , engineering , mathematics , paleontology , geometry , biology , operating system
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.